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TVM: Learning-based Learning Systems Tianqi Chen Paul G. Allen School of Computer Science & Engineering University of Washington

TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

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Page 1: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

TVM: Learning-based Learning Systems

Tianqi ChenPaul G. Allen School of Computer Science & Engineering

University of Washington

Page 2: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Machine Learning is Everywhere

Page 3: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Machine Learning is Everywhere

Page 4: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Machine Learning is Everywhere

Page 5: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Machine Learning is Everywhere

Page 6: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Abbreviated History of Machine Learning

Based on personal view

Page 7: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Abbreviated History of Machine Learning

1960

Based on personal view

Page 8: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Abbreviated History of Machine Learning

1960Research

Based on personal view

Page 9: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Abbreviated History of Machine Learning

1960ResearchLSTM

ConvNet GBMSVM

Based on personal view

Page 10: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Abbreviated History of Machine Learning

1960ResearchLSTM

ConvNet GBMSVM

2000

Based on personal view

Page 11: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Abbreviated History of Machine Learning

1960

Data

ResearchLSTMConvNet GBMSVM

2000

Based on personal view

Page 12: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Abbreviated History of Machine Learning

1960

Data

ResearchLSTMConvNet GBMSVM

2000 MTurk

Based on personal view

Page 13: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Abbreviated History of Machine Learning

1960

Data

ResearchLSTMConvNet GBMSVM

2000 MTurk

Based on personal view

Page 14: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Abbreviated History of Machine Learning

1960

Data

ResearchLSTMConvNet GBMSVM

2000 MTurk

Based on personal view

Page 15: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Abbreviated History of Machine Learning

1960

Data

ResearchLSTMConvNet GBMSVM

2000

2006

MTurk

Based on personal view

Page 16: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Abbreviated History of Machine Learning

1960

Data

Hardware

ResearchLSTMConvNet GBMSVM

2000

2006

MTurk

Based on personal view

Page 17: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Abbreviated History of Machine Learning

1960

Data

Hardware

ResearchLSTMConvNet GBMSVM

2000

2006

Public Cloud

MTurk

Based on personal view

Page 18: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Abbreviated History of Machine Learning

1960

Data

Hardware

ResearchLSTMConvNet GBMSVM

2000

2006

Public Cloud

MTurk

Based on personal view

Page 19: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Abbreviated History of Machine Learning

1960

Data

Hardware

ResearchLSTMConvNet GBMSVM

2000

2006

Public Cloud

MTurk

Based on personal view

Page 20: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Abbreviated History of Machine Learning

1960

Data

Hardware

ResearchLSTMConvNet GBMSVM

2000

2010

2006

Public Cloud

MTurk

Based on personal view

Page 21: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Abbreviated History of Machine Learning

1960

Data

Hardware

ResearchLSTMConvNet GBMSVM

2000

2010

2006

Public Cloud

ML Systems

MTurk

Based on personal view

Page 22: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Abbreviated History of Machine Learning

1960

Data

Hardware

ResearchLSTMConvNet GBMSVM

2000

2010

2006

Public Cloud

ML Systems

MTurk

Based on personal view

Page 23: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Abbreviated History of Machine Learning

1960

Data

Hardware

ResearchLSTMConvNet GBMSVM

2000

2010

2006

Public Cloud

ML Systems

MTurk

Based on personal view

Page 24: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Abbreviated History of Machine Learning

1960

Data

Hardware

ResearchLSTMConvNet GBMSVM

2000

2010

2006

Public Cloud

ML Systems

MTurk

Based on personal view

Page 25: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Abbreviated History of Machine Learning

1960

Data

Hardware

ResearchLSTMConvNet GBMSVM

2000

2010

2006

Public Cloud

ML Systems

MTurk

Based on personal view

Page 26: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Abbreviated History of Machine Learning

1960

Data

Hardware

ResearchLSTMConvNet GBMSVM

2000

2010

2006

Public Cloud

ML Systems

MTurk

Based on personal view

Page 27: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Abbreviated History of Machine Learning

1960

Data

Hardware

ResearchLSTMConvNet GBMSVM

2000

2010

2006

Public Cloud

ML Systems

MTurk

Based on personal view

Page 28: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Abbreviated History of Machine Learning

1960

Data

Hardware

ResearchLSTMConvNet GBMSVM

2000

2010

2006

Public Cloud

ML Systems

MTurk

Based on personal view

Page 29: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Learning Systems

Data science for everyone

Scale up deep learning

Deploy AI everywhere

Page 30: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Learning Systems

Data science for everyone

Scale up deep learning

Deploy AI everywhere

Accessible and scalable learning systems

Page 31: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Learning Systems

Data science for everyone

Scale up deep learning

Deploy AI everywhere

Accessible and scalable learning systems

Page 32: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Current Learning Systems

Systems

Page 33: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Current Learning Systems

Systems Machine Learning

Enables

Page 34: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Current Learning Systems

Systems Machine Learning

Enables

Optimize

Page 35: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Current Learning Systems

Systems Machine Learning

Enables

Optimize

Me

Page 36: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Current Learning Systems

Systems Machine Learning

Enables

Optimize

Me

Can we use ML to optimize systems

Page 37: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Learning-based Learning Systems

Machine LearningSystems

Enables

Page 38: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Learning-based Learning Systems

Machine LearningSystems

Enables

Optimizes

Page 39: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Learning-based Learning Systems

Machine LearningSystems

Enables

Optimizes

Page 40: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

My Research

Data science for everyone

Scale up deep learning

Deploy AI everywhere

TVM: An Automated End-to-End Optimizing Compiler for Deep Learning. Chen et al. OSDI 18

Learning to Optimize Tensor Programs. Chen et al. NeurIPS 18

Page 41: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

My Research

Data science for everyone

Scale up deep learning

Deploy AI everywhere

TVM: An Automated End-to-End Optimizing Compiler for Deep Learning. Chen et al. OSDI 18

Learning to Optimize Tensor Programs. Chen et al. NeurIPS 18

Page 42: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

TVM: Learning-based Learning System

Why do we need machine learning for systems

How to build intelligent systems with learning

End to end learning-based learning system stack

Page 43: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

TVM: Learning-based Learning System

Why do we need machine learning for systems

How to build intelligent systems with learning

End to end learning-based learning system stack

Page 44: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Problem: Deep Learning Deployment

Model

Hardware Backends

Page 45: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Problem: Deep Learning Deployment

Model

Hardware Backends

Deploy

Page 46: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

How did I Start to Work on This Problem?

Page 47: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Acclerator

How did I Start to Work on This Problem?

Page 48: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Acclerator

How did I Start to Work on This Problem?

Page 49: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Acclerator

// Pseudo-code for convolution program for the VIA accelerator// Virtual Thread 00x00: LOAD(PARAM[ 0-71]) // LD@TID00x01: LOAD(ACTIV[ 0-24]) // LD@TID00x02: LOAD(LDBUF[ 0-31]) // LD@TID00x03: PUSH(LD->EX) // LD@TID00x04: POP (LD->EX) // EX@TID00x05: EXE (ACTIV[ 0-24],PARAM[ 0-71],LDBUF[ 0-31],STBUF[ 0- 7]) // EX@TID00x06: PUSH(EX->LD) // EX@TID00x07: PUSH(EX->ST) // EX@TID00x08: POP (EX->ST) // ST@TID00x09: STOR(STBUF[ 0- 7]) // ST@TID00x0A: PUSH(ST->EX) // ST@TID0// Virtual Thread 10x0B: LOAD(ACTIV[25-50]) // LD@TID10x0C: LOAD(LDBUF[32-63]) // LD@TID10x0D: PUSH(LD->EX) // LD@TID10x0E: POP (LD->EX) // EX@TID10x0F: EXE (ACTIV[25-50],PARAM[ 0-71],LDBUF[32-63],STBUF[32-39]) // EX@TID10x10: PUSH(EX->LD) // EX@TID10x11: PUSH(EX->ST) // EX@TID10x12: POP (EX->ST) // ST@TID10x13: STOR(STBUF[32-39]) // ST@TID10x14: PUSH(ST->EX) // ST@TID1// Virtual Thread 20x15: POP (EX->LD) // LD@TID20x16: LOAD(PARAM[ 0-71]) // LD@TID20x17: LOAD(ACTIV[ 0-24]) // LD@TID20x18: LOAD(LDBUF[ 0-31]) // LD@TID20x19: PUSH(LD->EX) // LD@TID20x1A: POP (LD->EX) // EX@TID20x1B: POP (ST->EX) // EX@TID20x1C: EXE (ACTIV[ 0-24],PARAM[ 0-71],LDBUF[ 0-31],STBUF[ 0- 7]) // EX@TID20x1D: PUSH(EX->ST) // EX@TID20x1E: POP (EX->ST) // ST@TID20x1F: STOR(STBUF[ 0- 7]) // ST@TID2// Virtual Thread 30x20: POP (EX->LD) // LD@TID30x21: LOAD(ACTIV[25-50]) // LD@TID30x22: LOAD(LDBUF[32-63]) // LD@TID30x23: PUSH(LD->EX) // LD@TID30x24: POP (LD->EX) // EX@TID30x25: POP (ST->EX) // EX@TID20x26: EXE (ACTIV[25-50],PARAM[ 0-71],LDBUF[32-63],STBUF[32-39]) // EX@TID30x27: PUSH(EX->ST) // EX@TID30x28: POP (EX->ST) // ST@TID30x29: STOR(STBUF[32-39]) // ST@TID3

// Convolution access pattern dictated by micro-coded program.// Each register index is derived as a 2-D affine function.// e.g. idxrf = arfy+brfx+crf

0, where crf0 is specified by

// micro op 0 fields.for y in [0…i) for x in [0…j) rf[idxrf

0] += GEVM(act[idxact0], par[idxpar

0]) rf[idxrf

1] += GEVM(act[idxact1], par[idxpar

1]) … rf[idxrf

n] += GEVM(act[idxactn], par[idxpar

n])

(b) Convolution micro-coded program

// Max-pool, batch normalization and activation function// access pattern dictated by micro-coded program.// Each register index is derived as a 2D affine function.// e.g. idxdst = adsty+bdstx+cdst

0, where cdst0 is specified by

// micro op 0 fields.for y in [0…i) for x in [0…j) // max pooling rf[idxdst

0] = MAX(rf[idxdst0], rf[idxsrc

0]) rf[idxdst

1] = MAX(rf[idxdst1], rf[idxsrc

1]) … // batch norm rf[idxdst

m] = MUL(rf[idxdstm], rf[idxsrc

m]) rf[idxdst

m+1] = ADD(rf[idxdstm+1], rf[idxsrc

m+1]) rf[idxdst

m+2] = MUL(rf[idxdstm+2], rf[idxsrc

m+2]) rf[idxdst

m+3] = ADD(rf[idxdstm+3], rf[idxsrc

m+3]) … // activation rf[idxdst

n-1] = RELU(rf[idxdstn-1], rf[idxsrc

n-1]) rf[idxdst

n] = RELU(rf[idxdstn], rf[idxsrc

n])

(c) Max pool, batch norm and activationmicro-coded program

(a) Blocked convolution program with multiple thread contexts

How did I Start to Work on This Problem?

Page 50: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Frameworks

Deploy Deep Learning Everywhere

Hardware

Page 51: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Frameworks

Deploy Deep Learning Everywhere

Hardware

Page 52: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Frameworks

Deploy Deep Learning Everywhere

Hardware

Page 53: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Frameworks

Deploy Deep Learning Everywhere

Hardware

Page 54: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Frameworks

Deploy Deep Learning Everywhere

Hardware

Page 55: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Frameworks

Deploy Deep Learning Everywhere

Hardware

Page 56: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Frameworks

Deploy Deep Learning Everywhere

Hardware

Page 57: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Huge gap between model/frameworks and hardware backends

Frameworks

Deploy Deep Learning Everywhere

Hardware

Page 58: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Existing Deep Learning Frameworks

Hardware

Frameworks

Page 59: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Existing Deep Learning Frameworks

High-level data flow graph

Hardware

Frameworks

Page 60: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Existing Deep Learning Frameworks

High-level data flow graph

Hardware

Primitive Tensor operators such as Conv2D

Frameworks

Page 61: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Existing Deep Learning Frameworks

High-level data flow graph

Hardware

Primitive Tensor operators such as Conv2D

eg. cuDNN Offload to heavily optimized DNN operator library

Frameworks

Page 62: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Limitations of Existing Approach

cuDNN

Frameworks

Page 63: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Limitations of Existing Approach

cuDNN

Frameworks

Page 64: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Limitations of Existing Approach

cuDNN

Frameworks

Page 65: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Limitations of Existing Approach

cuDNN

Frameworks

Page 66: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Limitations of Existing Approach

cuDNN

Frameworks

Page 67: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Limitations of Existing Approach

cuDNN

Frameworks

New operator introduced by operator fusion optimization potential benefit: 1.5x speedup

Page 68: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Limitations of Existing Approach

cuDNN

Frameworks

New operator introduced by operator fusion optimization potential benefit: 1.5x speedup

Page 69: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Limitations of Existing Approach

cuDNN

Frameworks

New operator introduced by operator fusion optimization potential benefit: 1.5x speedup

Page 70: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Limitations of Existing Approach

cuDNN

Frameworks

New operator introduced by operator fusion optimization potential benefit: 1.5x speedup

Engineering intensive

Page 71: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Learning-based Learning System

High-level data flow graph and optimizations

Hardware

Frameworks

Page 72: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Learning-based Learning System

High-level data flow graph and optimizations

Hardware

Frameworks

Page 73: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Machine Learning based Program Optimizer

Learning-based Learning System

High-level data flow graph and optimizations

Hardware

Frameworks

Page 74: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Machine Learning based Program Optimizer

Learning-based Learning System

High-level data flow graph and optimizations

Directly generate optimized program for new operator workloads and hardware

Hardware

Frameworks

Page 75: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

TVM: Learning-based Learning System

Why do we need machine learning for systems

How to build intelligent systems with learning

End to end learning-based learning system stack

Page 76: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Problem Setting

Page 77: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Problem Setting

C = tvm.compute((m, n), lambda y, x: tvm.sum(A[k, y] * B[k, x], axis=k))

Tensor Expression (Specification)

Page 78: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Problem Setting

C = tvm.compute((m, n), lambda y, x: tvm.sum(A[k, y] * B[k, x], axis=k))

Tensor Expression (Specification)

Search Space of Possible Program Optimizations

Page 79: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Problem Setting

C = tvm.compute((m, n), lambda y, x: tvm.sum(A[k, y] * B[k, x], axis=k))

Tensor Expression (Specification)

Search Space of Possible Program Optimizations

for y in range(1024): for x in range(1024): C[y][x] = 0 for k in range(1024): C[y][x] += A[k][y] * B[k][x]

inp_buffer AL[8][8], BL[8][8]acc_buffer CL[8][8]for yo in range(128): for xo in range(128): vdla.fill_zero(CL) for ko in range(128): vdla.dma_copy2d(AL, A[ko*8:ko*8+8][yo*8:yo*8+8]) vdla.dma_copy2d(BL, B[ko*8:ko*8+8][xo*8:xo*8+8]) vdla.fused_gemm8x8_add(CL, AL, BL) vdla.dma_copy2d(C[yo*8:yo*8+8,xo*8:xo*8+8], CL)

for yo in range(128): for xo in range(128): C[yo*8:yo*8+8][xo*8:xo*8+8] = 0 for ko in range(128): for yi in range(8): for xi in range(8): for ki in range(8): C[yo*8+yi][xo*8+xi] += A[ko*8+ki][yo*8+yi] * B[ko*8+ki][xo*8+xi]

Low-level Program Variants

Page 80: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Example Instance in a Search Space

!18

C = tvm.compute((m, n), lambda y, x: tvm.sum(A[k, y] * B[k, x], axis=k))

Tensor Expression (Specification)

Search Space of Possible Program Optimizations

Page 81: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Example Instance in a Search Space

!18

for y in range(1024): for x in range(1024): C[y][x] = 0 for k in range(1024): C[y][x] += A[k][y] * B[k][x]

Vanilla Code

C = tvm.compute((m, n), lambda y, x: tvm.sum(A[k, y] * B[k, x], axis=k))

Tensor Expression (Specification)

Search Space of Possible Program Optimizations

Page 82: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Example Instance in a Search Space

!19

for yo in range(128): for xo in range(128): C[yo*8:yo*8+8][xo*8:xo*8+8] = 0 for ko in range(128): for yi in range(8): for xi in range(8): for ki in range(8): C[yo*8+yi][xo*8+xi] += A[ko*8+ki][yo*8+yi] * B[ko*8+ki][xo*8+xi]

Loop Tiling for Locality

C = tvm.compute((m, n), lambda y, x: tvm.sum(A[k, y] * B[k, x], axis=k))

Tensor Expression (Specification)

Search Space of Possible Program Optimizations

Page 83: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Example Instance in a Search Space

!20

inp_buffer AL[8][8], BL[8][8]acc_buffer CL[8][8]for yo in range(128): for xo in range(128): vdla.fill_zero(CL) for ko in range(128): vdla.dma_copy2d(AL, A[ko*8:ko*8+8][yo*8:yo*8+8]) vdla.dma_copy2d(BL, B[ko*8:ko*8+8][xo*8:xo*8+8]) vdla.fused_gemm8x8_add(CL, AL, BL) vdla.dma_copy2d(C[yo*8:yo*8+8,xo*8:xo*8+8], CL)

Map to Accelerators

C = tvm.compute((m, n), lambda y, x: tvm.sum(A[k, y] * B[k, x], axis=k))

Tensor Expression (Specification)

Search Space of Possible Program Optimizations

Page 84: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Optimization Choices in a Search Space

Loop Transformations

Thread Bindings

Cache Locality

Thread Cooperation Tensorization Latency

Hiding

C = tvm.compute((m, n), lambda y, x: tvm.sum(A[k, y] * B[k, x], axis=k))

Tensor Expression (Specification)

More details about the search space in the second half of the talk

Page 85: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Optimization Choices in a Search Space

Loop Transformations

Thread Bindings

Cache Locality

Thread Cooperation Tensorization Latency

Hiding

C = tvm.compute((m, n), lambda y, x: tvm.sum(A[k, y] * B[k, x], axis=k))

Tensor Expression (Specification)

More details about the search space in the second half of the talk

Page 86: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Optimization Choices in a Search Space

Loop Transformations

Thread Bindings

Cache Locality

Thread Cooperation Tensorization Latency

Hiding

Billions of possible optimization choices

C = tvm.compute((m, n), lambda y, x: tvm.sum(A[k, y] * B[k, x], axis=k))

Tensor Expression (Specification)

More details about the search space in the second half of the talk

Page 87: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Problem Formalization

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Search SpaceSe<latexit sha1_base64="tAVAC9U/qBVzQqUzJzuAwTDLLCw=">AAAB9HicbVDLSgMxFL1TX7W+qi7dBIvgqsyIoMuiG5cV7QPaoWTS2zY0kxmTTKEM/Q43LhRx68e482/MtLPQ1gOBwzn3ck9OEAuujet+O4W19Y3NreJ2aWd3b/+gfHjU1FGiGDZYJCLVDqhGwSU2DDcC27FCGgYCW8H4NvNbE1SaR/LRTGP0QzqUfMAZNVbyuyE1I0ZF+jDrYa9ccavuHGSVeDmpQI56r/zV7UcsCVEaJqjWHc+NjZ9SZTgTOCt1E40xZWM6xI6lkoao/XQeekbOrNIng0jZJw2Zq783UhpqPQ0DO5mF1MteJv7ndRIzuPZTLuPEoGSLQ4NEEBORrAHS5wqZEVNLKFPcZiVsRBVlxvZUsiV4y19eJc2LqudWvfvLSu0mr6MIJ3AK5+DBFdTgDurQAAZP8Ayv8OZMnBfn3flYjBacfOcY/sD5/AEIKZJB</latexit><latexit sha1_base64="tAVAC9U/qBVzQqUzJzuAwTDLLCw=">AAAB9HicbVDLSgMxFL1TX7W+qi7dBIvgqsyIoMuiG5cV7QPaoWTS2zY0kxmTTKEM/Q43LhRx68e482/MtLPQ1gOBwzn3ck9OEAuujet+O4W19Y3NreJ2aWd3b/+gfHjU1FGiGDZYJCLVDqhGwSU2DDcC27FCGgYCW8H4NvNbE1SaR/LRTGP0QzqUfMAZNVbyuyE1I0ZF+jDrYa9ccavuHGSVeDmpQI56r/zV7UcsCVEaJqjWHc+NjZ9SZTgTOCt1E40xZWM6xI6lkoao/XQeekbOrNIng0jZJw2Zq783UhpqPQ0DO5mF1MteJv7ndRIzuPZTLuPEoGSLQ4NEEBORrAHS5wqZEVNLKFPcZiVsRBVlxvZUsiV4y19eJc2LqudWvfvLSu0mr6MIJ3AK5+DBFdTgDurQAAZP8Ayv8OZMnBfn3flYjBacfOcY/sD5/AEIKZJB</latexit><latexit sha1_base64="tAVAC9U/qBVzQqUzJzuAwTDLLCw=">AAAB9HicbVDLSgMxFL1TX7W+qi7dBIvgqsyIoMuiG5cV7QPaoWTS2zY0kxmTTKEM/Q43LhRx68e482/MtLPQ1gOBwzn3ck9OEAuujet+O4W19Y3NreJ2aWd3b/+gfHjU1FGiGDZYJCLVDqhGwSU2DDcC27FCGgYCW8H4NvNbE1SaR/LRTGP0QzqUfMAZNVbyuyE1I0ZF+jDrYa9ccavuHGSVeDmpQI56r/zV7UcsCVEaJqjWHc+NjZ9SZTgTOCt1E40xZWM6xI6lkoao/XQeekbOrNIng0jZJw2Zq783UhpqPQ0DO5mF1MteJv7ndRIzuPZTLuPEoGSLQ4NEEBORrAHS5wqZEVNLKFPcZiVsRBVlxvZUsiV4y19eJc2LqudWvfvLSu0mr6MIJ3AK5+DBFdTgDurQAAZP8Ayv8OZMnBfn3flYjBacfOcY/sD5/AEIKZJB</latexit><latexit sha1_base64="tAVAC9U/qBVzQqUzJzuAwTDLLCw=">AAAB9HicbVDLSgMxFL1TX7W+qi7dBIvgqsyIoMuiG5cV7QPaoWTS2zY0kxmTTKEM/Q43LhRx68e482/MtLPQ1gOBwzn3ck9OEAuujet+O4W19Y3NreJ2aWd3b/+gfHjU1FGiGDZYJCLVDqhGwSU2DDcC27FCGgYCW8H4NvNbE1SaR/LRTGP0QzqUfMAZNVbyuyE1I0ZF+jDrYa9ccavuHGSVeDmpQI56r/zV7UcsCVEaJqjWHc+NjZ9SZTgTOCt1E40xZWM6xI6lkoao/XQeekbOrNIng0jZJw2Zq783UhpqPQ0DO5mF1MteJv7ndRIzuPZTLuPEoGSLQ4NEEBORrAHS5wqZEVNLKFPcZiVsRBVlxvZUsiV4y19eJc2LqudWvfvLSu0mr6MIJ3AK5+DBFdTgDurQAAZP8Ayv8OZMnBfn3flYjBacfOcY/sD5/AEIKZJB</latexit>

Expression

Page 88: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Problem Formalization

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Expression AutoTVM

Page 89: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Problem Formalization

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Search SpaceSe<latexit sha1_base64="tAVAC9U/qBVzQqUzJzuAwTDLLCw=">AAAB9HicbVDLSgMxFL1TX7W+qi7dBIvgqsyIoMuiG5cV7QPaoWTS2zY0kxmTTKEM/Q43LhRx68e482/MtLPQ1gOBwzn3ck9OEAuujet+O4W19Y3NreJ2aWd3b/+gfHjU1FGiGDZYJCLVDqhGwSU2DDcC27FCGgYCW8H4NvNbE1SaR/LRTGP0QzqUfMAZNVbyuyE1I0ZF+jDrYa9ccavuHGSVeDmpQI56r/zV7UcsCVEaJqjWHc+NjZ9SZTgTOCt1E40xZWM6xI6lkoao/XQeekbOrNIng0jZJw2Zq783UhpqPQ0DO5mF1MteJv7ndRIzuPZTLuPEoGSLQ4NEEBORrAHS5wqZEVNLKFPcZiVsRBVlxvZUsiV4y19eJc2LqudWvfvLSu0mr6MIJ3AK5+DBFdTgDurQAAZP8Ayv8OZMnBfn3flYjBacfOcY/sD5/AEIKZJB</latexit><latexit sha1_base64="tAVAC9U/qBVzQqUzJzuAwTDLLCw=">AAAB9HicbVDLSgMxFL1TX7W+qi7dBIvgqsyIoMuiG5cV7QPaoWTS2zY0kxmTTKEM/Q43LhRx68e482/MtLPQ1gOBwzn3ck9OEAuujet+O4W19Y3NreJ2aWd3b/+gfHjU1FGiGDZYJCLVDqhGwSU2DDcC27FCGgYCW8H4NvNbE1SaR/LRTGP0QzqUfMAZNVbyuyE1I0ZF+jDrYa9ccavuHGSVeDmpQI56r/zV7UcsCVEaJqjWHc+NjZ9SZTgTOCt1E40xZWM6xI6lkoao/XQeekbOrNIng0jZJw2Zq783UhpqPQ0DO5mF1MteJv7ndRIzuPZTLuPEoGSLQ4NEEBORrAHS5wqZEVNLKFPcZiVsRBVlxvZUsiV4y19eJc2LqudWvfvLSu0mr6MIJ3AK5+DBFdTgDurQAAZP8Ayv8OZMnBfn3flYjBacfOcY/sD5/AEIKZJB</latexit><latexit sha1_base64="tAVAC9U/qBVzQqUzJzuAwTDLLCw=">AAAB9HicbVDLSgMxFL1TX7W+qi7dBIvgqsyIoMuiG5cV7QPaoWTS2zY0kxmTTKEM/Q43LhRx68e482/MtLPQ1gOBwzn3ck9OEAuujet+O4W19Y3NreJ2aWd3b/+gfHjU1FGiGDZYJCLVDqhGwSU2DDcC27FCGgYCW8H4NvNbE1SaR/LRTGP0QzqUfMAZNVbyuyE1I0ZF+jDrYa9ccavuHGSVeDmpQI56r/zV7UcsCVEaJqjWHc+NjZ9SZTgTOCt1E40xZWM6xI6lkoao/XQeekbOrNIng0jZJw2Zq783UhpqPQ0DO5mF1MteJv7ndRIzuPZTLuPEoGSLQ4NEEBORrAHS5wqZEVNLKFPcZiVsRBVlxvZUsiV4y19eJc2LqudWvfvLSu0mr6MIJ3AK5+DBFdTgDurQAAZP8Ayv8OZMnBfn3flYjBacfOcY/sD5/AEIKZJB</latexit><latexit sha1_base64="tAVAC9U/qBVzQqUzJzuAwTDLLCw=">AAAB9HicbVDLSgMxFL1TX7W+qi7dBIvgqsyIoMuiG5cV7QPaoWTS2zY0kxmTTKEM/Q43LhRx68e482/MtLPQ1gOBwzn3ck9OEAuujet+O4W19Y3NreJ2aWd3b/+gfHjU1FGiGDZYJCLVDqhGwSU2DDcC27FCGgYCW8H4NvNbE1SaR/LRTGP0QzqUfMAZNVbyuyE1I0ZF+jDrYa9ccavuHGSVeDmpQI56r/zV7UcsCVEaJqjWHc+NjZ9SZTgTOCt1E40xZWM6xI6lkoao/XQeekbOrNIng0jZJw2Zq783UhpqPQ0DO5mF1MteJv7ndRIzuPZTLuPEoGSLQ4NEEBORrAHS5wqZEVNLKFPcZiVsRBVlxvZUsiV4y19eJc2LqudWvfvLSu0mr6MIJ3AK5+DBFdTgDurQAAZP8Ayv8OZMnBfn3flYjBacfOcY/sD5/AEIKZJB</latexit>

Expression AutoTVM Code Generatorc<latexit sha1_base64="MK9TkR1XfIASB2dfp9XX0/+fbPE=">AAAB6HicbVBNS8NAEJ3Ur1q/qh69LBbBU0lEqMeiF48t2A9oQ9lsJ+3azSbsboQS+gu8eFDEqz/Jm//GbZuDtj4YeLw3w8y8IBFcG9f9dgobm1vbO8Xd0t7+weFR+fikreNUMWyxWMSqG1CNgktsGW4EdhOFNAoEdoLJ3dzvPKHSPJYPZpqgH9GR5CFn1FipyQblilt1FyDrxMtJBXI0BuWv/jBmaYTSMEG17nluYvyMKsOZwFmpn2pMKJvQEfYslTRC7WeLQ2fkwipDEsbKljRkof6eyGik9TQKbGdEzVivenPxP6+XmvDGz7hMUoOSLReFqSAmJvOvyZArZEZMLaFMcXsrYWOqKDM2m5INwVt9eZ20r6qeW/Wa15X6bR5HEc7gHC7BgxrU4R4a0AIGCM/wCm/Oo/PivDsfy9aCk8+cwh84nz/GZYzn</latexit><latexit sha1_base64="MK9TkR1XfIASB2dfp9XX0/+fbPE=">AAAB6HicbVBNS8NAEJ3Ur1q/qh69LBbBU0lEqMeiF48t2A9oQ9lsJ+3azSbsboQS+gu8eFDEqz/Jm//GbZuDtj4YeLw3w8y8IBFcG9f9dgobm1vbO8Xd0t7+weFR+fikreNUMWyxWMSqG1CNgktsGW4EdhOFNAoEdoLJ3dzvPKHSPJYPZpqgH9GR5CFn1FipyQblilt1FyDrxMtJBXI0BuWv/jBmaYTSMEG17nluYvyMKsOZwFmpn2pMKJvQEfYslTRC7WeLQ2fkwipDEsbKljRkof6eyGik9TQKbGdEzVivenPxP6+XmvDGz7hMUoOSLReFqSAmJvOvyZArZEZMLaFMcXsrYWOqKDM2m5INwVt9eZ20r6qeW/Wa15X6bR5HEc7gHC7BgxrU4R4a0AIGCM/wCm/Oo/PivDsfy9aCk8+cwh84nz/GZYzn</latexit><latexit sha1_base64="MK9TkR1XfIASB2dfp9XX0/+fbPE=">AAAB6HicbVBNS8NAEJ3Ur1q/qh69LBbBU0lEqMeiF48t2A9oQ9lsJ+3azSbsboQS+gu8eFDEqz/Jm//GbZuDtj4YeLw3w8y8IBFcG9f9dgobm1vbO8Xd0t7+weFR+fikreNUMWyxWMSqG1CNgktsGW4EdhOFNAoEdoLJ3dzvPKHSPJYPZpqgH9GR5CFn1FipyQblilt1FyDrxMtJBXI0BuWv/jBmaYTSMEG17nluYvyMKsOZwFmpn2pMKJvQEfYslTRC7WeLQ2fkwipDEsbKljRkof6eyGik9TQKbGdEzVivenPxP6+XmvDGz7hMUoOSLReFqSAmJvOvyZArZEZMLaFMcXsrYWOqKDM2m5INwVt9eZ20r6qeW/Wa15X6bR5HEc7gHC7BgxrU4R4a0AIGCM/wCm/Oo/PivDsfy9aCk8+cwh84nz/GZYzn</latexit><latexit sha1_base64="MK9TkR1XfIASB2dfp9XX0/+fbPE=">AAAB6HicbVBNS8NAEJ3Ur1q/qh69LBbBU0lEqMeiF48t2A9oQ9lsJ+3azSbsboQS+gu8eFDEqz/Jm//GbZuDtj4YeLw3w8y8IBFcG9f9dgobm1vbO8Xd0t7+weFR+fikreNUMWyxWMSqG1CNgktsGW4EdhOFNAoEdoLJ3dzvPKHSPJYPZpqgH9GR5CFn1FipyQblilt1FyDrxMtJBXI0BuWv/jBmaYTSMEG17nluYvyMKsOZwFmpn2pMKJvQEfYslTRC7WeLQ2fkwipDEsbKljRkof6eyGik9TQKbGdEzVivenPxP6+XmvDGz7hMUoOSLReFqSAmJvOvyZArZEZMLaFMcXsrYWOqKDM2m5INwVt9eZ20r6qeW/Wa15X6bR5HEc7gHC7BgxrU4R4a0AIGCM/wCm/Oo/PivDsfy9aCk8+cwh84nz/GZYzn</latexit>

Optimization Configuration

x = g(e, c)<latexit sha1_base64="3Iwz7sxhu2857QlhPoWgkaq4hyw=">AAAB8nicbVBNSwMxEM3Wr1q/qh69BItQQcquCHoRil48VrAfsF1KNs22odlkSWbFsvRnePGgiFd/jTf/jWm7B219MPB4b4aZeWEiuAHX/XYKK6tr6xvFzdLW9s7uXnn/oGVUqilrUiWU7oTEMMElawIHwTqJZiQOBWuHo9up335k2nAlH2CcsCAmA8kjTglYyX/C13hQZWeYnvbKFbfmzoCXiZeTCsrR6JW/un1F05hJoIIY43tuAkFGNHAq2KTUTQ1LCB2RAfMtlSRmJshmJ0/wiVX6OFLalgQ8U39PZCQ2ZhyHtjMmMDSL3lT8z/NTiK6CjMskBSbpfFGUCgwKT//Hfa4ZBTG2hFDN7a2YDokmFGxKJRuCt/jyMmmd1zy35t1fVOo3eRxFdISOURV56BLV0R1qoCaiSKFn9IreHHBenHfnY95acPKZQ/QHzucP3O+PqQ==</latexit><latexit sha1_base64="3Iwz7sxhu2857QlhPoWgkaq4hyw=">AAAB8nicbVBNSwMxEM3Wr1q/qh69BItQQcquCHoRil48VrAfsF1KNs22odlkSWbFsvRnePGgiFd/jTf/jWm7B219MPB4b4aZeWEiuAHX/XYKK6tr6xvFzdLW9s7uXnn/oGVUqilrUiWU7oTEMMElawIHwTqJZiQOBWuHo9up335k2nAlH2CcsCAmA8kjTglYyX/C13hQZWeYnvbKFbfmzoCXiZeTCsrR6JW/un1F05hJoIIY43tuAkFGNHAq2KTUTQ1LCB2RAfMtlSRmJshmJ0/wiVX6OFLalgQ8U39PZCQ2ZhyHtjMmMDSL3lT8z/NTiK6CjMskBSbpfFGUCgwKT//Hfa4ZBTG2hFDN7a2YDokmFGxKJRuCt/jyMmmd1zy35t1fVOo3eRxFdISOURV56BLV0R1qoCaiSKFn9IreHHBenHfnY95acPKZQ/QHzucP3O+PqQ==</latexit><latexit sha1_base64="3Iwz7sxhu2857QlhPoWgkaq4hyw=">AAAB8nicbVBNSwMxEM3Wr1q/qh69BItQQcquCHoRil48VrAfsF1KNs22odlkSWbFsvRnePGgiFd/jTf/jWm7B219MPB4b4aZeWEiuAHX/XYKK6tr6xvFzdLW9s7uXnn/oGVUqilrUiWU7oTEMMElawIHwTqJZiQOBWuHo9up335k2nAlH2CcsCAmA8kjTglYyX/C13hQZWeYnvbKFbfmzoCXiZeTCsrR6JW/un1F05hJoIIY43tuAkFGNHAq2KTUTQ1LCB2RAfMtlSRmJshmJ0/wiVX6OFLalgQ8U39PZCQ2ZhyHtjMmMDSL3lT8z/NTiK6CjMskBSbpfFGUCgwKT//Hfa4ZBTG2hFDN7a2YDokmFGxKJRuCt/jyMmmd1zy35t1fVOo3eRxFdISOURV56BLV0R1qoCaiSKFn9IreHHBenHfnY95acPKZQ/QHzucP3O+PqQ==</latexit><latexit sha1_base64="3Iwz7sxhu2857QlhPoWgkaq4hyw=">AAAB8nicbVBNSwMxEM3Wr1q/qh69BItQQcquCHoRil48VrAfsF1KNs22odlkSWbFsvRnePGgiFd/jTf/jWm7B219MPB4b4aZeWEiuAHX/XYKK6tr6xvFzdLW9s7uXnn/oGVUqilrUiWU7oTEMMElawIHwTqJZiQOBWuHo9up335k2nAlH2CcsCAmA8kjTglYyX/C13hQZWeYnvbKFbfmzoCXiZeTCsrR6JW/un1F05hJoIIY43tuAkFGNHAq2KTUTQ1LCB2RAfMtlSRmJshmJ0/wiVX6OFLalgQ8U39PZCQ2ZhyHtjMmMDSL3lT8z/NTiK6CjMskBSbpfFGUCgwKT//Hfa4ZBTG2hFDN7a2YDokmFGxKJRuCt/jyMmmd1zy35t1fVOo3eRxFdISOURV56BLV0R1qoCaiSKFn9IreHHBenHfnY95acPKZQ/QHzucP3O+PqQ==</latexit>

Page 90: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Problem Formalization

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Search SpaceSe<latexit sha1_base64="tAVAC9U/qBVzQqUzJzuAwTDLLCw=">AAAB9HicbVDLSgMxFL1TX7W+qi7dBIvgqsyIoMuiG5cV7QPaoWTS2zY0kxmTTKEM/Q43LhRx68e482/MtLPQ1gOBwzn3ck9OEAuujet+O4W19Y3NreJ2aWd3b/+gfHjU1FGiGDZYJCLVDqhGwSU2DDcC27FCGgYCW8H4NvNbE1SaR/LRTGP0QzqUfMAZNVbyuyE1I0ZF+jDrYa9ccavuHGSVeDmpQI56r/zV7UcsCVEaJqjWHc+NjZ9SZTgTOCt1E40xZWM6xI6lkoao/XQeekbOrNIng0jZJw2Zq783UhpqPQ0DO5mF1MteJv7ndRIzuPZTLuPEoGSLQ4NEEBORrAHS5wqZEVNLKFPcZiVsRBVlxvZUsiV4y19eJc2LqudWvfvLSu0mr6MIJ3AK5+DBFdTgDurQAAZP8Ayv8OZMnBfn3flYjBacfOcY/sD5/AEIKZJB</latexit><latexit sha1_base64="tAVAC9U/qBVzQqUzJzuAwTDLLCw=">AAAB9HicbVDLSgMxFL1TX7W+qi7dBIvgqsyIoMuiG5cV7QPaoWTS2zY0kxmTTKEM/Q43LhRx68e482/MtLPQ1gOBwzn3ck9OEAuujet+O4W19Y3NreJ2aWd3b/+gfHjU1FGiGDZYJCLVDqhGwSU2DDcC27FCGgYCW8H4NvNbE1SaR/LRTGP0QzqUfMAZNVbyuyE1I0ZF+jDrYa9ccavuHGSVeDmpQI56r/zV7UcsCVEaJqjWHc+NjZ9SZTgTOCt1E40xZWM6xI6lkoao/XQeekbOrNIng0jZJw2Zq783UhpqPQ0DO5mF1MteJv7ndRIzuPZTLuPEoGSLQ4NEEBORrAHS5wqZEVNLKFPcZiVsRBVlxvZUsiV4y19eJc2LqudWvfvLSu0mr6MIJ3AK5+DBFdTgDurQAAZP8Ayv8OZMnBfn3flYjBacfOcY/sD5/AEIKZJB</latexit><latexit sha1_base64="tAVAC9U/qBVzQqUzJzuAwTDLLCw=">AAAB9HicbVDLSgMxFL1TX7W+qi7dBIvgqsyIoMuiG5cV7QPaoWTS2zY0kxmTTKEM/Q43LhRx68e482/MtLPQ1gOBwzn3ck9OEAuujet+O4W19Y3NreJ2aWd3b/+gfHjU1FGiGDZYJCLVDqhGwSU2DDcC27FCGgYCW8H4NvNbE1SaR/LRTGP0QzqUfMAZNVbyuyE1I0ZF+jDrYa9ccavuHGSVeDmpQI56r/zV7UcsCVEaJqjWHc+NjZ9SZTgTOCt1E40xZWM6xI6lkoao/XQeekbOrNIng0jZJw2Zq783UhpqPQ0DO5mF1MteJv7ndRIzuPZTLuPEoGSLQ4NEEBORrAHS5wqZEVNLKFPcZiVsRBVlxvZUsiV4y19eJc2LqudWvfvLSu0mr6MIJ3AK5+DBFdTgDurQAAZP8Ayv8OZMnBfn3flYjBacfOcY/sD5/AEIKZJB</latexit><latexit sha1_base64="tAVAC9U/qBVzQqUzJzuAwTDLLCw=">AAAB9HicbVDLSgMxFL1TX7W+qi7dBIvgqsyIoMuiG5cV7QPaoWTS2zY0kxmTTKEM/Q43LhRx68e482/MtLPQ1gOBwzn3ck9OEAuujet+O4W19Y3NreJ2aWd3b/+gfHjU1FGiGDZYJCLVDqhGwSU2DDcC27FCGgYCW8H4NvNbE1SaR/LRTGP0QzqUfMAZNVbyuyE1I0ZF+jDrYa9ccavuHGSVeDmpQI56r/zV7UcsCVEaJqjWHc+NjZ9SZTgTOCt1E40xZWM6xI6lkoao/XQeekbOrNIng0jZJw2Zq783UhpqPQ0DO5mF1MteJv7ndRIzuPZTLuPEoGSLQ4NEEBORrAHS5wqZEVNLKFPcZiVsRBVlxvZUsiV4y19eJc2LqudWvfvLSu0mr6MIJ3AK5+DBFdTgDurQAAZP8Ayv8OZMnBfn3flYjBacfOcY/sD5/AEIKZJB</latexit>

Expression AutoTVM

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x

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Program

Cost: Execute Time

Code Generatorc<latexit sha1_base64="MK9TkR1XfIASB2dfp9XX0/+fbPE=">AAAB6HicbVBNS8NAEJ3Ur1q/qh69LBbBU0lEqMeiF48t2A9oQ9lsJ+3azSbsboQS+gu8eFDEqz/Jm//GbZuDtj4YeLw3w8y8IBFcG9f9dgobm1vbO8Xd0t7+weFR+fikreNUMWyxWMSqG1CNgktsGW4EdhOFNAoEdoLJ3dzvPKHSPJYPZpqgH9GR5CFn1FipyQblilt1FyDrxMtJBXI0BuWv/jBmaYTSMEG17nluYvyMKsOZwFmpn2pMKJvQEfYslTRC7WeLQ2fkwipDEsbKljRkof6eyGik9TQKbGdEzVivenPxP6+XmvDGz7hMUoOSLReFqSAmJvOvyZArZEZMLaFMcXsrYWOqKDM2m5INwVt9eZ20r6qeW/Wa15X6bR5HEc7gHC7BgxrU4R4a0AIGCM/wCm/Oo/PivDsfy9aCk8+cwh84nz/GZYzn</latexit><latexit sha1_base64="MK9TkR1XfIASB2dfp9XX0/+fbPE=">AAAB6HicbVBNS8NAEJ3Ur1q/qh69LBbBU0lEqMeiF48t2A9oQ9lsJ+3azSbsboQS+gu8eFDEqz/Jm//GbZuDtj4YeLw3w8y8IBFcG9f9dgobm1vbO8Xd0t7+weFR+fikreNUMWyxWMSqG1CNgktsGW4EdhOFNAoEdoLJ3dzvPKHSPJYPZpqgH9GR5CFn1FipyQblilt1FyDrxMtJBXI0BuWv/jBmaYTSMEG17nluYvyMKsOZwFmpn2pMKJvQEfYslTRC7WeLQ2fkwipDEsbKljRkof6eyGik9TQKbGdEzVivenPxP6+XmvDGz7hMUoOSLReFqSAmJvOvyZArZEZMLaFMcXsrYWOqKDM2m5INwVt9eZ20r6qeW/Wa15X6bR5HEc7gHC7BgxrU4R4a0AIGCM/wCm/Oo/PivDsfy9aCk8+cwh84nz/GZYzn</latexit><latexit sha1_base64="MK9TkR1XfIASB2dfp9XX0/+fbPE=">AAAB6HicbVBNS8NAEJ3Ur1q/qh69LBbBU0lEqMeiF48t2A9oQ9lsJ+3azSbsboQS+gu8eFDEqz/Jm//GbZuDtj4YeLw3w8y8IBFcG9f9dgobm1vbO8Xd0t7+weFR+fikreNUMWyxWMSqG1CNgktsGW4EdhOFNAoEdoLJ3dzvPKHSPJYPZpqgH9GR5CFn1FipyQblilt1FyDrxMtJBXI0BuWv/jBmaYTSMEG17nluYvyMKsOZwFmpn2pMKJvQEfYslTRC7WeLQ2fkwipDEsbKljRkof6eyGik9TQKbGdEzVivenPxP6+XmvDGz7hMUoOSLReFqSAmJvOvyZArZEZMLaFMcXsrYWOqKDM2m5INwVt9eZ20r6qeW/Wa15X6bR5HEc7gHC7BgxrU4R4a0AIGCM/wCm/Oo/PivDsfy9aCk8+cwh84nz/GZYzn</latexit><latexit sha1_base64="MK9TkR1XfIASB2dfp9XX0/+fbPE=">AAAB6HicbVBNS8NAEJ3Ur1q/qh69LBbBU0lEqMeiF48t2A9oQ9lsJ+3azSbsboQS+gu8eFDEqz/Jm//GbZuDtj4YeLw3w8y8IBFcG9f9dgobm1vbO8Xd0t7+weFR+fikreNUMWyxWMSqG1CNgktsGW4EdhOFNAoEdoLJ3dzvPKHSPJYPZpqgH9GR5CFn1FipyQblilt1FyDrxMtJBXI0BuWv/jBmaYTSMEG17nluYvyMKsOZwFmpn2pMKJvQEfYslTRC7WeLQ2fkwipDEsbKljRkof6eyGik9TQKbGdEzVivenPxP6+XmvDGz7hMUoOSLReFqSAmJvOvyZArZEZMLaFMcXsrYWOqKDM2m5INwVt9eZ20r6qeW/Wa15X6bR5HEc7gHC7BgxrU4R4a0AIGCM/wCm/Oo/PivDsfy9aCk8+cwh84nz/GZYzn</latexit>

Optimization Configuration

x = g(e, c)<latexit sha1_base64="3Iwz7sxhu2857QlhPoWgkaq4hyw=">AAAB8nicbVBNSwMxEM3Wr1q/qh69BItQQcquCHoRil48VrAfsF1KNs22odlkSWbFsvRnePGgiFd/jTf/jWm7B219MPB4b4aZeWEiuAHX/XYKK6tr6xvFzdLW9s7uXnn/oGVUqilrUiWU7oTEMMElawIHwTqJZiQOBWuHo9up335k2nAlH2CcsCAmA8kjTglYyX/C13hQZWeYnvbKFbfmzoCXiZeTCsrR6JW/un1F05hJoIIY43tuAkFGNHAq2KTUTQ1LCB2RAfMtlSRmJshmJ0/wiVX6OFLalgQ8U39PZCQ2ZhyHtjMmMDSL3lT8z/NTiK6CjMskBSbpfFGUCgwKT//Hfa4ZBTG2hFDN7a2YDokmFGxKJRuCt/jyMmmd1zy35t1fVOo3eRxFdISOURV56BLV0R1qoCaiSKFn9IreHHBenHfnY95acPKZQ/QHzucP3O+PqQ==</latexit><latexit sha1_base64="3Iwz7sxhu2857QlhPoWgkaq4hyw=">AAAB8nicbVBNSwMxEM3Wr1q/qh69BItQQcquCHoRil48VrAfsF1KNs22odlkSWbFsvRnePGgiFd/jTf/jWm7B219MPB4b4aZeWEiuAHX/XYKK6tr6xvFzdLW9s7uXnn/oGVUqilrUiWU7oTEMMElawIHwTqJZiQOBWuHo9up335k2nAlH2CcsCAmA8kjTglYyX/C13hQZWeYnvbKFbfmzoCXiZeTCsrR6JW/un1F05hJoIIY43tuAkFGNHAq2KTUTQ1LCB2RAfMtlSRmJshmJ0/wiVX6OFLalgQ8U39PZCQ2ZhyHtjMmMDSL3lT8z/NTiK6CjMskBSbpfFGUCgwKT//Hfa4ZBTG2hFDN7a2YDokmFGxKJRuCt/jyMmmd1zy35t1fVOo3eRxFdISOURV56BLV0R1qoCaiSKFn9IreHHBenHfnY95acPKZQ/QHzucP3O+PqQ==</latexit><latexit sha1_base64="3Iwz7sxhu2857QlhPoWgkaq4hyw=">AAAB8nicbVBNSwMxEM3Wr1q/qh69BItQQcquCHoRil48VrAfsF1KNs22odlkSWbFsvRnePGgiFd/jTf/jWm7B219MPB4b4aZeWEiuAHX/XYKK6tr6xvFzdLW9s7uXnn/oGVUqilrUiWU7oTEMMElawIHwTqJZiQOBWuHo9up335k2nAlH2CcsCAmA8kjTglYyX/C13hQZWeYnvbKFbfmzoCXiZeTCsrR6JW/un1F05hJoIIY43tuAkFGNHAq2KTUTQ1LCB2RAfMtlSRmJshmJ0/wiVX6OFLalgQ8U39PZCQ2ZhyHtjMmMDSL3lT8z/NTiK6CjMskBSbpfFGUCgwKT//Hfa4ZBTG2hFDN7a2YDokmFGxKJRuCt/jyMmmd1zy35t1fVOo3eRxFdISOURV56BLV0R1qoCaiSKFn9IreHHBenHfnY95acPKZQ/QHzucP3O+PqQ==</latexit><latexit sha1_base64="3Iwz7sxhu2857QlhPoWgkaq4hyw=">AAAB8nicbVBNSwMxEM3Wr1q/qh69BItQQcquCHoRil48VrAfsF1KNs22odlkSWbFsvRnePGgiFd/jTf/jWm7B219MPB4b4aZeWEiuAHX/XYKK6tr6xvFzdLW9s7uXnn/oGVUqilrUiWU7oTEMMElawIHwTqJZiQOBWuHo9up335k2nAlH2CcsCAmA8kjTglYyX/C13hQZWeYnvbKFbfmzoCXiZeTCsrR6JW/un1F05hJoIIY43tuAkFGNHAq2KTUTQ1LCB2RAfMtlSRmJshmJ0/wiVX6OFLalgQ8U39PZCQ2ZhyHtjMmMDSL3lT8z/NTiK6CjMskBSbpfFGUCgwKT//Hfa4ZBTG2hFDN7a2YDokmFGxKJRuCt/jyMmmd1zy35t1fVOo3eRxFdISOURV56BLV0R1qoCaiSKFn9IreHHBenHfnY95acPKZQ/QHzucP3O+PqQ==</latexit>

Page 91: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Problem Formalization

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Search SpaceSe<latexit sha1_base64="tAVAC9U/qBVzQqUzJzuAwTDLLCw=">AAAB9HicbVDLSgMxFL1TX7W+qi7dBIvgqsyIoMuiG5cV7QPaoWTS2zY0kxmTTKEM/Q43LhRx68e482/MtLPQ1gOBwzn3ck9OEAuujet+O4W19Y3NreJ2aWd3b/+gfHjU1FGiGDZYJCLVDqhGwSU2DDcC27FCGgYCW8H4NvNbE1SaR/LRTGP0QzqUfMAZNVbyuyE1I0ZF+jDrYa9ccavuHGSVeDmpQI56r/zV7UcsCVEaJqjWHc+NjZ9SZTgTOCt1E40xZWM6xI6lkoao/XQeekbOrNIng0jZJw2Zq783UhpqPQ0DO5mF1MteJv7ndRIzuPZTLuPEoGSLQ4NEEBORrAHS5wqZEVNLKFPcZiVsRBVlxvZUsiV4y19eJc2LqudWvfvLSu0mr6MIJ3AK5+DBFdTgDurQAAZP8Ayv8OZMnBfn3flYjBacfOcY/sD5/AEIKZJB</latexit><latexit sha1_base64="tAVAC9U/qBVzQqUzJzuAwTDLLCw=">AAAB9HicbVDLSgMxFL1TX7W+qi7dBIvgqsyIoMuiG5cV7QPaoWTS2zY0kxmTTKEM/Q43LhRx68e482/MtLPQ1gOBwzn3ck9OEAuujet+O4W19Y3NreJ2aWd3b/+gfHjU1FGiGDZYJCLVDqhGwSU2DDcC27FCGgYCW8H4NvNbE1SaR/LRTGP0QzqUfMAZNVbyuyE1I0ZF+jDrYa9ccavuHGSVeDmpQI56r/zV7UcsCVEaJqjWHc+NjZ9SZTgTOCt1E40xZWM6xI6lkoao/XQeekbOrNIng0jZJw2Zq783UhpqPQ0DO5mF1MteJv7ndRIzuPZTLuPEoGSLQ4NEEBORrAHS5wqZEVNLKFPcZiVsRBVlxvZUsiV4y19eJc2LqudWvfvLSu0mr6MIJ3AK5+DBFdTgDurQAAZP8Ayv8OZMnBfn3flYjBacfOcY/sD5/AEIKZJB</latexit><latexit sha1_base64="tAVAC9U/qBVzQqUzJzuAwTDLLCw=">AAAB9HicbVDLSgMxFL1TX7W+qi7dBIvgqsyIoMuiG5cV7QPaoWTS2zY0kxmTTKEM/Q43LhRx68e482/MtLPQ1gOBwzn3ck9OEAuujet+O4W19Y3NreJ2aWd3b/+gfHjU1FGiGDZYJCLVDqhGwSU2DDcC27FCGgYCW8H4NvNbE1SaR/LRTGP0QzqUfMAZNVbyuyE1I0ZF+jDrYa9ccavuHGSVeDmpQI56r/zV7UcsCVEaJqjWHc+NjZ9SZTgTOCt1E40xZWM6xI6lkoao/XQeekbOrNIng0jZJw2Zq783UhpqPQ0DO5mF1MteJv7ndRIzuPZTLuPEoGSLQ4NEEBORrAHS5wqZEVNLKFPcZiVsRBVlxvZUsiV4y19eJc2LqudWvfvLSu0mr6MIJ3AK5+DBFdTgDurQAAZP8Ayv8OZMnBfn3flYjBacfOcY/sD5/AEIKZJB</latexit><latexit sha1_base64="tAVAC9U/qBVzQqUzJzuAwTDLLCw=">AAAB9HicbVDLSgMxFL1TX7W+qi7dBIvgqsyIoMuiG5cV7QPaoWTS2zY0kxmTTKEM/Q43LhRx68e482/MtLPQ1gOBwzn3ck9OEAuujet+O4W19Y3NreJ2aWd3b/+gfHjU1FGiGDZYJCLVDqhGwSU2DDcC27FCGgYCW8H4NvNbE1SaR/LRTGP0QzqUfMAZNVbyuyE1I0ZF+jDrYa9ccavuHGSVeDmpQI56r/zV7UcsCVEaJqjWHc+NjZ9SZTgTOCt1E40xZWM6xI6lkoao/XQeekbOrNIng0jZJw2Zq783UhpqPQ0DO5mF1MteJv7ndRIzuPZTLuPEoGSLQ4NEEBORrAHS5wqZEVNLKFPcZiVsRBVlxvZUsiV4y19eJc2LqudWvfvLSu0mr6MIJ3AK5+DBFdTgDurQAAZP8Ayv8OZMnBfn3flYjBacfOcY/sD5/AEIKZJB</latexit>

Expression AutoTVM

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x

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Program

Cost: Execute Time

Code Generatorc<latexit sha1_base64="MK9TkR1XfIASB2dfp9XX0/+fbPE=">AAAB6HicbVBNS8NAEJ3Ur1q/qh69LBbBU0lEqMeiF48t2A9oQ9lsJ+3azSbsboQS+gu8eFDEqz/Jm//GbZuDtj4YeLw3w8y8IBFcG9f9dgobm1vbO8Xd0t7+weFR+fikreNUMWyxWMSqG1CNgktsGW4EdhOFNAoEdoLJ3dzvPKHSPJYPZpqgH9GR5CFn1FipyQblilt1FyDrxMtJBXI0BuWv/jBmaYTSMEG17nluYvyMKsOZwFmpn2pMKJvQEfYslTRC7WeLQ2fkwipDEsbKljRkof6eyGik9TQKbGdEzVivenPxP6+XmvDGz7hMUoOSLReFqSAmJvOvyZArZEZMLaFMcXsrYWOqKDM2m5INwVt9eZ20r6qeW/Wa15X6bR5HEc7gHC7BgxrU4R4a0AIGCM/wCm/Oo/PivDsfy9aCk8+cwh84nz/GZYzn</latexit><latexit sha1_base64="MK9TkR1XfIASB2dfp9XX0/+fbPE=">AAAB6HicbVBNS8NAEJ3Ur1q/qh69LBbBU0lEqMeiF48t2A9oQ9lsJ+3azSbsboQS+gu8eFDEqz/Jm//GbZuDtj4YeLw3w8y8IBFcG9f9dgobm1vbO8Xd0t7+weFR+fikreNUMWyxWMSqG1CNgktsGW4EdhOFNAoEdoLJ3dzvPKHSPJYPZpqgH9GR5CFn1FipyQblilt1FyDrxMtJBXI0BuWv/jBmaYTSMEG17nluYvyMKsOZwFmpn2pMKJvQEfYslTRC7WeLQ2fkwipDEsbKljRkof6eyGik9TQKbGdEzVivenPxP6+XmvDGz7hMUoOSLReFqSAmJvOvyZArZEZMLaFMcXsrYWOqKDM2m5INwVt9eZ20r6qeW/Wa15X6bR5HEc7gHC7BgxrU4R4a0AIGCM/wCm/Oo/PivDsfy9aCk8+cwh84nz/GZYzn</latexit><latexit sha1_base64="MK9TkR1XfIASB2dfp9XX0/+fbPE=">AAAB6HicbVBNS8NAEJ3Ur1q/qh69LBbBU0lEqMeiF48t2A9oQ9lsJ+3azSbsboQS+gu8eFDEqz/Jm//GbZuDtj4YeLw3w8y8IBFcG9f9dgobm1vbO8Xd0t7+weFR+fikreNUMWyxWMSqG1CNgktsGW4EdhOFNAoEdoLJ3dzvPKHSPJYPZpqgH9GR5CFn1FipyQblilt1FyDrxMtJBXI0BuWv/jBmaYTSMEG17nluYvyMKsOZwFmpn2pMKJvQEfYslTRC7WeLQ2fkwipDEsbKljRkof6eyGik9TQKbGdEzVivenPxP6+XmvDGz7hMUoOSLReFqSAmJvOvyZArZEZMLaFMcXsrYWOqKDM2m5INwVt9eZ20r6qeW/Wa15X6bR5HEc7gHC7BgxrU4R4a0AIGCM/wCm/Oo/PivDsfy9aCk8+cwh84nz/GZYzn</latexit><latexit sha1_base64="MK9TkR1XfIASB2dfp9XX0/+fbPE=">AAAB6HicbVBNS8NAEJ3Ur1q/qh69LBbBU0lEqMeiF48t2A9oQ9lsJ+3azSbsboQS+gu8eFDEqz/Jm//GbZuDtj4YeLw3w8y8IBFcG9f9dgobm1vbO8Xd0t7+weFR+fikreNUMWyxWMSqG1CNgktsGW4EdhOFNAoEdoLJ3dzvPKHSPJYPZpqgH9GR5CFn1FipyQblilt1FyDrxMtJBXI0BuWv/jBmaYTSMEG17nluYvyMKsOZwFmpn2pMKJvQEfYslTRC7WeLQ2fkwipDEsbKljRkof6eyGik9TQKbGdEzVivenPxP6+XmvDGz7hMUoOSLReFqSAmJvOvyZArZEZMLaFMcXsrYWOqKDM2m5INwVt9eZ20r6qeW/Wa15X6bR5HEc7gHC7BgxrU4R4a0AIGCM/wCm/Oo/PivDsfy9aCk8+cwh84nz/GZYzn</latexit>

Optimization Configuration

x = g(e, c)<latexit sha1_base64="3Iwz7sxhu2857QlhPoWgkaq4hyw=">AAAB8nicbVBNSwMxEM3Wr1q/qh69BItQQcquCHoRil48VrAfsF1KNs22odlkSWbFsvRnePGgiFd/jTf/jWm7B219MPB4b4aZeWEiuAHX/XYKK6tr6xvFzdLW9s7uXnn/oGVUqilrUiWU7oTEMMElawIHwTqJZiQOBWuHo9up335k2nAlH2CcsCAmA8kjTglYyX/C13hQZWeYnvbKFbfmzoCXiZeTCsrR6JW/un1F05hJoIIY43tuAkFGNHAq2KTUTQ1LCB2RAfMtlSRmJshmJ0/wiVX6OFLalgQ8U39PZCQ2ZhyHtjMmMDSL3lT8z/NTiK6CjMskBSbpfFGUCgwKT//Hfa4ZBTG2hFDN7a2YDokmFGxKJRuCt/jyMmmd1zy35t1fVOo3eRxFdISOURV56BLV0R1qoCaiSKFn9IreHHBenHfnY95acPKZQ/QHzucP3O+PqQ==</latexit><latexit sha1_base64="3Iwz7sxhu2857QlhPoWgkaq4hyw=">AAAB8nicbVBNSwMxEM3Wr1q/qh69BItQQcquCHoRil48VrAfsF1KNs22odlkSWbFsvRnePGgiFd/jTf/jWm7B219MPB4b4aZeWEiuAHX/XYKK6tr6xvFzdLW9s7uXnn/oGVUqilrUiWU7oTEMMElawIHwTqJZiQOBWuHo9up335k2nAlH2CcsCAmA8kjTglYyX/C13hQZWeYnvbKFbfmzoCXiZeTCsrR6JW/un1F05hJoIIY43tuAkFGNHAq2KTUTQ1LCB2RAfMtlSRmJshmJ0/wiVX6OFLalgQ8U39PZCQ2ZhyHtjMmMDSL3lT8z/NTiK6CjMskBSbpfFGUCgwKT//Hfa4ZBTG2hFDN7a2YDokmFGxKJRuCt/jyMmmd1zy35t1fVOo3eRxFdISOURV56BLV0R1qoCaiSKFn9IreHHBenHfnY95acPKZQ/QHzucP3O+PqQ==</latexit><latexit sha1_base64="3Iwz7sxhu2857QlhPoWgkaq4hyw=">AAAB8nicbVBNSwMxEM3Wr1q/qh69BItQQcquCHoRil48VrAfsF1KNs22odlkSWbFsvRnePGgiFd/jTf/jWm7B219MPB4b4aZeWEiuAHX/XYKK6tr6xvFzdLW9s7uXnn/oGVUqilrUiWU7oTEMMElawIHwTqJZiQOBWuHo9up335k2nAlH2CcsCAmA8kjTglYyX/C13hQZWeYnvbKFbfmzoCXiZeTCsrR6JW/un1F05hJoIIY43tuAkFGNHAq2KTUTQ1LCB2RAfMtlSRmJshmJ0/wiVX6OFLalgQ8U39PZCQ2ZhyHtjMmMDSL3lT8z/NTiK6CjMskBSbpfFGUCgwKT//Hfa4ZBTG2hFDN7a2YDokmFGxKJRuCt/jyMmmd1zy35t1fVOo3eRxFdISOURV56BLV0R1qoCaiSKFn9IreHHBenHfnY95acPKZQ/QHzucP3O+PqQ==</latexit><latexit sha1_base64="3Iwz7sxhu2857QlhPoWgkaq4hyw=">AAAB8nicbVBNSwMxEM3Wr1q/qh69BItQQcquCHoRil48VrAfsF1KNs22odlkSWbFsvRnePGgiFd/jTf/jWm7B219MPB4b4aZeWEiuAHX/XYKK6tr6xvFzdLW9s7uXnn/oGVUqilrUiWU7oTEMMElawIHwTqJZiQOBWuHo9up335k2nAlH2CcsCAmA8kjTglYyX/C13hQZWeYnvbKFbfmzoCXiZeTCsrR6JW/un1F05hJoIIY43tuAkFGNHAq2KTUTQ1LCB2RAfMtlSRmJshmJ0/wiVX6OFLalgQ8U39PZCQ2ZhyHtjMmMDSL3lT8z/NTiK6CjMskBSbpfFGUCgwKT//Hfa4ZBTG2hFDN7a2YDokmFGxKJRuCt/jyMmmd1zy35t1fVOo3eRxFdISOURV56BLV0R1qoCaiSKFn9IreHHBenHfnY95acPKZQ/QHzucP3O+PqQ==</latexit>

Objective argminc2Sef(g(e, c))<latexit sha1_base64="ZCHsWwERxsuuK3AxdLGw0zZnNkw=">AAACB3icbVDLSsNAFJ3UV62vqktBBovQipREBF0W3bisaB/QhDCZ3rRDJ5MwMxFK6M6Nv+LGhSJu/QV3/o3Tx0JbD1w4nHMv994TJJwpbdvfVm5peWV1Lb9e2Njc2t4p7u41VZxKCg0a81i2A6KAMwENzTSHdiKBRAGHVjC4HvutB5CKxeJeDxPwItITLGSUaCP5xUMie+5JxISfUZcJfOfDCIflXhlOMa1U/GLJrtoT4EXizEgJzVD3i19uN6ZpBEJTTpTqOHaivYxIzSiHUcFNFSSEDkgPOoYKEoHysskfI3xslC4OY2lKaDxRf09kJFJqGAWmMyK6r+a9sfif10l1eOllTCSpBkGni8KUYx3jcSi4yyRQzYeGECqZuRXTPpGEahNdwYTgzL+8SJpnVceuOrfnpdrVLI48OkBHqIwcdIFq6AbVUQNR9Iie0St6s56sF+vd+pi25qzZzD76A+vzB1DOl6I=</latexit><latexit sha1_base64="ZCHsWwERxsuuK3AxdLGw0zZnNkw=">AAACB3icbVDLSsNAFJ3UV62vqktBBovQipREBF0W3bisaB/QhDCZ3rRDJ5MwMxFK6M6Nv+LGhSJu/QV3/o3Tx0JbD1w4nHMv994TJJwpbdvfVm5peWV1Lb9e2Njc2t4p7u41VZxKCg0a81i2A6KAMwENzTSHdiKBRAGHVjC4HvutB5CKxeJeDxPwItITLGSUaCP5xUMie+5JxISfUZcJfOfDCIflXhlOMa1U/GLJrtoT4EXizEgJzVD3i19uN6ZpBEJTTpTqOHaivYxIzSiHUcFNFSSEDkgPOoYKEoHysskfI3xslC4OY2lKaDxRf09kJFJqGAWmMyK6r+a9sfif10l1eOllTCSpBkGni8KUYx3jcSi4yyRQzYeGECqZuRXTPpGEahNdwYTgzL+8SJpnVceuOrfnpdrVLI48OkBHqIwcdIFq6AbVUQNR9Iie0St6s56sF+vd+pi25qzZzD76A+vzB1DOl6I=</latexit><latexit sha1_base64="ZCHsWwERxsuuK3AxdLGw0zZnNkw=">AAACB3icbVDLSsNAFJ3UV62vqktBBovQipREBF0W3bisaB/QhDCZ3rRDJ5MwMxFK6M6Nv+LGhSJu/QV3/o3Tx0JbD1w4nHMv994TJJwpbdvfVm5peWV1Lb9e2Njc2t4p7u41VZxKCg0a81i2A6KAMwENzTSHdiKBRAGHVjC4HvutB5CKxeJeDxPwItITLGSUaCP5xUMie+5JxISfUZcJfOfDCIflXhlOMa1U/GLJrtoT4EXizEgJzVD3i19uN6ZpBEJTTpTqOHaivYxIzSiHUcFNFSSEDkgPOoYKEoHysskfI3xslC4OY2lKaDxRf09kJFJqGAWmMyK6r+a9sfif10l1eOllTCSpBkGni8KUYx3jcSi4yyRQzYeGECqZuRXTPpGEahNdwYTgzL+8SJpnVceuOrfnpdrVLI48OkBHqIwcdIFq6AbVUQNR9Iie0St6s56sF+vd+pi25qzZzD76A+vzB1DOl6I=</latexit><latexit sha1_base64="ZCHsWwERxsuuK3AxdLGw0zZnNkw=">AAACB3icbVDLSsNAFJ3UV62vqktBBovQipREBF0W3bisaB/QhDCZ3rRDJ5MwMxFK6M6Nv+LGhSJu/QV3/o3Tx0JbD1w4nHMv994TJJwpbdvfVm5peWV1Lb9e2Njc2t4p7u41VZxKCg0a81i2A6KAMwENzTSHdiKBRAGHVjC4HvutB5CKxeJeDxPwItITLGSUaCP5xUMie+5JxISfUZcJfOfDCIflXhlOMa1U/GLJrtoT4EXizEgJzVD3i19uN6ZpBEJTTpTqOHaivYxIzSiHUcFNFSSEDkgPOoYKEoHysskfI3xslC4OY2lKaDxRf09kJFJqGAWmMyK6r+a9sfif10l1eOllTCSpBkGni8KUYx3jcSi4yyRQzYeGECqZuRXTPpGEahNdwYTgzL+8SJpnVceuOrfnpdrVLI48OkBHqIwcdIFq6AbVUQNR9Iie0St6s56sF+vd+pi25qzZzD76A+vzB1DOl6I=</latexit>

Page 92: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Black-box OptimizationTry each configuration until we find a good one

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Search Space

Expression AutoTVM

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Page 93: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Black-box OptimizationTry each configuration until we find a good one

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Expression AutoTVM Code Generatorc<latexit sha1_base64="MK9TkR1XfIASB2dfp9XX0/+fbPE=">AAAB6HicbVBNS8NAEJ3Ur1q/qh69LBbBU0lEqMeiF48t2A9oQ9lsJ+3azSbsboQS+gu8eFDEqz/Jm//GbZuDtj4YeLw3w8y8IBFcG9f9dgobm1vbO8Xd0t7+weFR+fikreNUMWyxWMSqG1CNgktsGW4EdhOFNAoEdoLJ3dzvPKHSPJYPZpqgH9GR5CFn1FipyQblilt1FyDrxMtJBXI0BuWv/jBmaYTSMEG17nluYvyMKsOZwFmpn2pMKJvQEfYslTRC7WeLQ2fkwipDEsbKljRkof6eyGik9TQKbGdEzVivenPxP6+XmvDGz7hMUoOSLReFqSAmJvOvyZArZEZMLaFMcXsrYWOqKDM2m5INwVt9eZ20r6qeW/Wa15X6bR5HEc7gHC7BgxrU4R4a0AIGCM/wCm/Oo/PivDsfy9aCk8+cwh84nz/GZYzn</latexit><latexit sha1_base64="MK9TkR1XfIASB2dfp9XX0/+fbPE=">AAAB6HicbVBNS8NAEJ3Ur1q/qh69LBbBU0lEqMeiF48t2A9oQ9lsJ+3azSbsboQS+gu8eFDEqz/Jm//GbZuDtj4YeLw3w8y8IBFcG9f9dgobm1vbO8Xd0t7+weFR+fikreNUMWyxWMSqG1CNgktsGW4EdhOFNAoEdoLJ3dzvPKHSPJYPZpqgH9GR5CFn1FipyQblilt1FyDrxMtJBXI0BuWv/jBmaYTSMEG17nluYvyMKsOZwFmpn2pMKJvQEfYslTRC7WeLQ2fkwipDEsbKljRkof6eyGik9TQKbGdEzVivenPxP6+XmvDGz7hMUoOSLReFqSAmJvOvyZArZEZMLaFMcXsrYWOqKDM2m5INwVt9eZ20r6qeW/Wa15X6bR5HEc7gHC7BgxrU4R4a0AIGCM/wCm/Oo/PivDsfy9aCk8+cwh84nz/GZYzn</latexit><latexit sha1_base64="MK9TkR1XfIASB2dfp9XX0/+fbPE=">AAAB6HicbVBNS8NAEJ3Ur1q/qh69LBbBU0lEqMeiF48t2A9oQ9lsJ+3azSbsboQS+gu8eFDEqz/Jm//GbZuDtj4YeLw3w8y8IBFcG9f9dgobm1vbO8Xd0t7+weFR+fikreNUMWyxWMSqG1CNgktsGW4EdhOFNAoEdoLJ3dzvPKHSPJYPZpqgH9GR5CFn1FipyQblilt1FyDrxMtJBXI0BuWv/jBmaYTSMEG17nluYvyMKsOZwFmpn2pMKJvQEfYslTRC7WeLQ2fkwipDEsbKljRkof6eyGik9TQKbGdEzVivenPxP6+XmvDGz7hMUoOSLReFqSAmJvOvyZArZEZMLaFMcXsrYWOqKDM2m5INwVt9eZ20r6qeW/Wa15X6bR5HEc7gHC7BgxrU4R4a0AIGCM/wCm/Oo/PivDsfy9aCk8+cwh84nz/GZYzn</latexit><latexit sha1_base64="MK9TkR1XfIASB2dfp9XX0/+fbPE=">AAAB6HicbVBNS8NAEJ3Ur1q/qh69LBbBU0lEqMeiF48t2A9oQ9lsJ+3azSbsboQS+gu8eFDEqz/Jm//GbZuDtj4YeLw3w8y8IBFcG9f9dgobm1vbO8Xd0t7+weFR+fikreNUMWyxWMSqG1CNgktsGW4EdhOFNAoEdoLJ3dzvPKHSPJYPZpqgH9GR5CFn1FipyQblilt1FyDrxMtJBXI0BuWv/jBmaYTSMEG17nluYvyMKsOZwFmpn2pMKJvQEfYslTRC7WeLQ2fkwipDEsbKljRkof6eyGik9TQKbGdEzVivenPxP6+XmvDGz7hMUoOSLReFqSAmJvOvyZArZEZMLaFMcXsrYWOqKDM2m5INwVt9eZ20r6qeW/Wa15X6bR5HEc7gHC7BgxrU4R4a0AIGCM/wCm/Oo/PivDsfy9aCk8+cwh84nz/GZYzn</latexit>

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Page 94: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Black-box OptimizationTry each configuration until we find a good one

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Expression AutoTVM Code Generatorc<latexit sha1_base64="MK9TkR1XfIASB2dfp9XX0/+fbPE=">AAAB6HicbVBNS8NAEJ3Ur1q/qh69LBbBU0lEqMeiF48t2A9oQ9lsJ+3azSbsboQS+gu8eFDEqz/Jm//GbZuDtj4YeLw3w8y8IBFcG9f9dgobm1vbO8Xd0t7+weFR+fikreNUMWyxWMSqG1CNgktsGW4EdhOFNAoEdoLJ3dzvPKHSPJYPZpqgH9GR5CFn1FipyQblilt1FyDrxMtJBXI0BuWv/jBmaYTSMEG17nluYvyMKsOZwFmpn2pMKJvQEfYslTRC7WeLQ2fkwipDEsbKljRkof6eyGik9TQKbGdEzVivenPxP6+XmvDGz7hMUoOSLReFqSAmJvOvyZArZEZMLaFMcXsrYWOqKDM2m5INwVt9eZ20r6qeW/Wa15X6bR5HEc7gHC7BgxrU4R4a0AIGCM/wCm/Oo/PivDsfy9aCk8+cwh84nz/GZYzn</latexit><latexit sha1_base64="MK9TkR1XfIASB2dfp9XX0/+fbPE=">AAAB6HicbVBNS8NAEJ3Ur1q/qh69LBbBU0lEqMeiF48t2A9oQ9lsJ+3azSbsboQS+gu8eFDEqz/Jm//GbZuDtj4YeLw3w8y8IBFcG9f9dgobm1vbO8Xd0t7+weFR+fikreNUMWyxWMSqG1CNgktsGW4EdhOFNAoEdoLJ3dzvPKHSPJYPZpqgH9GR5CFn1FipyQblilt1FyDrxMtJBXI0BuWv/jBmaYTSMEG17nluYvyMKsOZwFmpn2pMKJvQEfYslTRC7WeLQ2fkwipDEsbKljRkof6eyGik9TQKbGdEzVivenPxP6+XmvDGz7hMUoOSLReFqSAmJvOvyZArZEZMLaFMcXsrYWOqKDM2m5INwVt9eZ20r6qeW/Wa15X6bR5HEc7gHC7BgxrU4R4a0AIGCM/wCm/Oo/PivDsfy9aCk8+cwh84nz/GZYzn</latexit><latexit sha1_base64="MK9TkR1XfIASB2dfp9XX0/+fbPE=">AAAB6HicbVBNS8NAEJ3Ur1q/qh69LBbBU0lEqMeiF48t2A9oQ9lsJ+3azSbsboQS+gu8eFDEqz/Jm//GbZuDtj4YeLw3w8y8IBFcG9f9dgobm1vbO8Xd0t7+weFR+fikreNUMWyxWMSqG1CNgktsGW4EdhOFNAoEdoLJ3dzvPKHSPJYPZpqgH9GR5CFn1FipyQblilt1FyDrxMtJBXI0BuWv/jBmaYTSMEG17nluYvyMKsOZwFmpn2pMKJvQEfYslTRC7WeLQ2fkwipDEsbKljRkof6eyGik9TQKbGdEzVivenPxP6+XmvDGz7hMUoOSLReFqSAmJvOvyZArZEZMLaFMcXsrYWOqKDM2m5INwVt9eZ20r6qeW/Wa15X6bR5HEc7gHC7BgxrU4R4a0AIGCM/wCm/Oo/PivDsfy9aCk8+cwh84nz/GZYzn</latexit><latexit sha1_base64="MK9TkR1XfIASB2dfp9XX0/+fbPE=">AAAB6HicbVBNS8NAEJ3Ur1q/qh69LBbBU0lEqMeiF48t2A9oQ9lsJ+3azSbsboQS+gu8eFDEqz/Jm//GbZuDtj4YeLw3w8y8IBFcG9f9dgobm1vbO8Xd0t7+weFR+fikreNUMWyxWMSqG1CNgktsGW4EdhOFNAoEdoLJ3dzvPKHSPJYPZpqgH9GR5CFn1FipyQblilt1FyDrxMtJBXI0BuWv/jBmaYTSMEG17nluYvyMKsOZwFmpn2pMKJvQEfYslTRC7WeLQ2fkwipDEsbKljRkof6eyGik9TQKbGdEzVivenPxP6+XmvDGz7hMUoOSLReFqSAmJvOvyZArZEZMLaFMcXsrYWOqKDM2m5INwVt9eZ20r6qeW/Wa15X6bR5HEc7gHC7BgxrU4R4a0AIGCM/wCm/Oo/PivDsfy9aCk8+cwh84nz/GZYzn</latexit>

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Page 95: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Black-box OptimizationTry each configuration until we find a good one

Challenge: lots of experimental trials, each trial costs ~1 second

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Page 96: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Cost-model Driven ApproachUse cost model to pick configuration

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Search Space

Expression AutoTVM

Page 97: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Cost-model Driven ApproachUse cost model to pick configuration

Cost Model

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Search Space

Expression AutoTVM

Page 98: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Cost-model Driven ApproachUse cost model to pick configuration

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Cost Model

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Search Space

Expression AutoTVM

Page 99: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Cost-model Driven ApproachUse cost model to pick configuration

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Cost Model

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Expression AutoTVM Code Generatorc<latexit sha1_base64="MK9TkR1XfIASB2dfp9XX0/+fbPE=">AAAB6HicbVBNS8NAEJ3Ur1q/qh69LBbBU0lEqMeiF48t2A9oQ9lsJ+3azSbsboQS+gu8eFDEqz/Jm//GbZuDtj4YeLw3w8y8IBFcG9f9dgobm1vbO8Xd0t7+weFR+fikreNUMWyxWMSqG1CNgktsGW4EdhOFNAoEdoLJ3dzvPKHSPJYPZpqgH9GR5CFn1FipyQblilt1FyDrxMtJBXI0BuWv/jBmaYTSMEG17nluYvyMKsOZwFmpn2pMKJvQEfYslTRC7WeLQ2fkwipDEsbKljRkof6eyGik9TQKbGdEzVivenPxP6+XmvDGz7hMUoOSLReFqSAmJvOvyZArZEZMLaFMcXsrYWOqKDM2m5INwVt9eZ20r6qeW/Wa15X6bR5HEc7gHC7BgxrU4R4a0AIGCM/wCm/Oo/PivDsfy9aCk8+cwh84nz/GZYzn</latexit><latexit sha1_base64="MK9TkR1XfIASB2dfp9XX0/+fbPE=">AAAB6HicbVBNS8NAEJ3Ur1q/qh69LBbBU0lEqMeiF48t2A9oQ9lsJ+3azSbsboQS+gu8eFDEqz/Jm//GbZuDtj4YeLw3w8y8IBFcG9f9dgobm1vbO8Xd0t7+weFR+fikreNUMWyxWMSqG1CNgktsGW4EdhOFNAoEdoLJ3dzvPKHSPJYPZpqgH9GR5CFn1FipyQblilt1FyDrxMtJBXI0BuWv/jBmaYTSMEG17nluYvyMKsOZwFmpn2pMKJvQEfYslTRC7WeLQ2fkwipDEsbKljRkof6eyGik9TQKbGdEzVivenPxP6+XmvDGz7hMUoOSLReFqSAmJvOvyZArZEZMLaFMcXsrYWOqKDM2m5INwVt9eZ20r6qeW/Wa15X6bR5HEc7gHC7BgxrU4R4a0AIGCM/wCm/Oo/PivDsfy9aCk8+cwh84nz/GZYzn</latexit><latexit sha1_base64="MK9TkR1XfIASB2dfp9XX0/+fbPE=">AAAB6HicbVBNS8NAEJ3Ur1q/qh69LBbBU0lEqMeiF48t2A9oQ9lsJ+3azSbsboQS+gu8eFDEqz/Jm//GbZuDtj4YeLw3w8y8IBFcG9f9dgobm1vbO8Xd0t7+weFR+fikreNUMWyxWMSqG1CNgktsGW4EdhOFNAoEdoLJ3dzvPKHSPJYPZpqgH9GR5CFn1FipyQblilt1FyDrxMtJBXI0BuWv/jBmaYTSMEG17nluYvyMKsOZwFmpn2pMKJvQEfYslTRC7WeLQ2fkwipDEsbKljRkof6eyGik9TQKbGdEzVivenPxP6+XmvDGz7hMUoOSLReFqSAmJvOvyZArZEZMLaFMcXsrYWOqKDM2m5INwVt9eZ20r6qeW/Wa15X6bR5HEc7gHC7BgxrU4R4a0AIGCM/wCm/Oo/PivDsfy9aCk8+cwh84nz/GZYzn</latexit><latexit sha1_base64="MK9TkR1XfIASB2dfp9XX0/+fbPE=">AAAB6HicbVBNS8NAEJ3Ur1q/qh69LBbBU0lEqMeiF48t2A9oQ9lsJ+3azSbsboQS+gu8eFDEqz/Jm//GbZuDtj4YeLw3w8y8IBFcG9f9dgobm1vbO8Xd0t7+weFR+fikreNUMWyxWMSqG1CNgktsGW4EdhOFNAoEdoLJ3dzvPKHSPJYPZpqgH9GR5CFn1FipyQblilt1FyDrxMtJBXI0BuWv/jBmaYTSMEG17nluYvyMKsOZwFmpn2pMKJvQEfYslTRC7WeLQ2fkwipDEsbKljRkof6eyGik9TQKbGdEzVivenPxP6+XmvDGz7hMUoOSLReFqSAmJvOvyZArZEZMLaFMcXsrYWOqKDM2m5INwVt9eZ20r6qeW/Wa15X6bR5HEc7gHC7BgxrU4R4a0AIGCM/wCm/Oo/PivDsfy9aCk8+cwh84nz/GZYzn</latexit>

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Page 100: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Cost-model Driven ApproachUse cost model to pick configuration

Challenge: Need reliable cost model per hardware

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Cost Model

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Expression AutoTVM Code Generatorc<latexit sha1_base64="MK9TkR1XfIASB2dfp9XX0/+fbPE=">AAAB6HicbVBNS8NAEJ3Ur1q/qh69LBbBU0lEqMeiF48t2A9oQ9lsJ+3azSbsboQS+gu8eFDEqz/Jm//GbZuDtj4YeLw3w8y8IBFcG9f9dgobm1vbO8Xd0t7+weFR+fikreNUMWyxWMSqG1CNgktsGW4EdhOFNAoEdoLJ3dzvPKHSPJYPZpqgH9GR5CFn1FipyQblilt1FyDrxMtJBXI0BuWv/jBmaYTSMEG17nluYvyMKsOZwFmpn2pMKJvQEfYslTRC7WeLQ2fkwipDEsbKljRkof6eyGik9TQKbGdEzVivenPxP6+XmvDGz7hMUoOSLReFqSAmJvOvyZArZEZMLaFMcXsrYWOqKDM2m5INwVt9eZ20r6qeW/Wa15X6bR5HEc7gHC7BgxrU4R4a0AIGCM/wCm/Oo/PivDsfy9aCk8+cwh84nz/GZYzn</latexit><latexit sha1_base64="MK9TkR1XfIASB2dfp9XX0/+fbPE=">AAAB6HicbVBNS8NAEJ3Ur1q/qh69LBbBU0lEqMeiF48t2A9oQ9lsJ+3azSbsboQS+gu8eFDEqz/Jm//GbZuDtj4YeLw3w8y8IBFcG9f9dgobm1vbO8Xd0t7+weFR+fikreNUMWyxWMSqG1CNgktsGW4EdhOFNAoEdoLJ3dzvPKHSPJYPZpqgH9GR5CFn1FipyQblilt1FyDrxMtJBXI0BuWv/jBmaYTSMEG17nluYvyMKsOZwFmpn2pMKJvQEfYslTRC7WeLQ2fkwipDEsbKljRkof6eyGik9TQKbGdEzVivenPxP6+XmvDGz7hMUoOSLReFqSAmJvOvyZArZEZMLaFMcXsrYWOqKDM2m5INwVt9eZ20r6qeW/Wa15X6bR5HEc7gHC7BgxrU4R4a0AIGCM/wCm/Oo/PivDsfy9aCk8+cwh84nz/GZYzn</latexit><latexit sha1_base64="MK9TkR1XfIASB2dfp9XX0/+fbPE=">AAAB6HicbVBNS8NAEJ3Ur1q/qh69LBbBU0lEqMeiF48t2A9oQ9lsJ+3azSbsboQS+gu8eFDEqz/Jm//GbZuDtj4YeLw3w8y8IBFcG9f9dgobm1vbO8Xd0t7+weFR+fikreNUMWyxWMSqG1CNgktsGW4EdhOFNAoEdoLJ3dzvPKHSPJYPZpqgH9GR5CFn1FipyQblilt1FyDrxMtJBXI0BuWv/jBmaYTSMEG17nluYvyMKsOZwFmpn2pMKJvQEfYslTRC7WeLQ2fkwipDEsbKljRkof6eyGik9TQKbGdEzVivenPxP6+XmvDGz7hMUoOSLReFqSAmJvOvyZArZEZMLaFMcXsrYWOqKDM2m5INwVt9eZ20r6qeW/Wa15X6bR5HEc7gHC7BgxrU4R4a0AIGCM/wCm/Oo/PivDsfy9aCk8+cwh84nz/GZYzn</latexit><latexit sha1_base64="MK9TkR1XfIASB2dfp9XX0/+fbPE=">AAAB6HicbVBNS8NAEJ3Ur1q/qh69LBbBU0lEqMeiF48t2A9oQ9lsJ+3azSbsboQS+gu8eFDEqz/Jm//GbZuDtj4YeLw3w8y8IBFcG9f9dgobm1vbO8Xd0t7+weFR+fikreNUMWyxWMSqG1CNgktsGW4EdhOFNAoEdoLJ3dzvPKHSPJYPZpqgH9GR5CFn1FipyQblilt1FyDrxMtJBXI0BuWv/jBmaYTSMEG17nluYvyMKsOZwFmpn2pMKJvQEfYslTRC7WeLQ2fkwipDEsbKljRkof6eyGik9TQKbGdEzVivenPxP6+XmvDGz7hMUoOSLReFqSAmJvOvyZArZEZMLaFMcXsrYWOqKDM2m5INwVt9eZ20r6qeW/Wa15X6bR5HEc7gHC7BgxrU4R4a0AIGCM/wCm/Oo/PivDsfy9aCk8+cwh84nz/GZYzn</latexit>

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Statistical Cost ModelUse machine learning to learn a statistical cost model

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Page 102: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Statistical Cost ModelUse machine learning to learn a statistical cost model

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Expression AutoTVM Code Generatorc<latexit sha1_base64="MK9TkR1XfIASB2dfp9XX0/+fbPE=">AAAB6HicbVBNS8NAEJ3Ur1q/qh69LBbBU0lEqMeiF48t2A9oQ9lsJ+3azSbsboQS+gu8eFDEqz/Jm//GbZuDtj4YeLw3w8y8IBFcG9f9dgobm1vbO8Xd0t7+weFR+fikreNUMWyxWMSqG1CNgktsGW4EdhOFNAoEdoLJ3dzvPKHSPJYPZpqgH9GR5CFn1FipyQblilt1FyDrxMtJBXI0BuWv/jBmaYTSMEG17nluYvyMKsOZwFmpn2pMKJvQEfYslTRC7WeLQ2fkwipDEsbKljRkof6eyGik9TQKbGdEzVivenPxP6+XmvDGz7hMUoOSLReFqSAmJvOvyZArZEZMLaFMcXsrYWOqKDM2m5INwVt9eZ20r6qeW/Wa15X6bR5HEc7gHC7BgxrU4R4a0AIGCM/wCm/Oo/PivDsfy9aCk8+cwh84nz/GZYzn</latexit><latexit sha1_base64="MK9TkR1XfIASB2dfp9XX0/+fbPE=">AAAB6HicbVBNS8NAEJ3Ur1q/qh69LBbBU0lEqMeiF48t2A9oQ9lsJ+3azSbsboQS+gu8eFDEqz/Jm//GbZuDtj4YeLw3w8y8IBFcG9f9dgobm1vbO8Xd0t7+weFR+fikreNUMWyxWMSqG1CNgktsGW4EdhOFNAoEdoLJ3dzvPKHSPJYPZpqgH9GR5CFn1FipyQblilt1FyDrxMtJBXI0BuWv/jBmaYTSMEG17nluYvyMKsOZwFmpn2pMKJvQEfYslTRC7WeLQ2fkwipDEsbKljRkof6eyGik9TQKbGdEzVivenPxP6+XmvDGz7hMUoOSLReFqSAmJvOvyZArZEZMLaFMcXsrYWOqKDM2m5INwVt9eZ20r6qeW/Wa15X6bR5HEc7gHC7BgxrU4R4a0AIGCM/wCm/Oo/PivDsfy9aCk8+cwh84nz/GZYzn</latexit><latexit sha1_base64="MK9TkR1XfIASB2dfp9XX0/+fbPE=">AAAB6HicbVBNS8NAEJ3Ur1q/qh69LBbBU0lEqMeiF48t2A9oQ9lsJ+3azSbsboQS+gu8eFDEqz/Jm//GbZuDtj4YeLw3w8y8IBFcG9f9dgobm1vbO8Xd0t7+weFR+fikreNUMWyxWMSqG1CNgktsGW4EdhOFNAoEdoLJ3dzvPKHSPJYPZpqgH9GR5CFn1FipyQblilt1FyDrxMtJBXI0BuWv/jBmaYTSMEG17nluYvyMKsOZwFmpn2pMKJvQEfYslTRC7WeLQ2fkwipDEsbKljRkof6eyGik9TQKbGdEzVivenPxP6+XmvDGz7hMUoOSLReFqSAmJvOvyZArZEZMLaFMcXsrYWOqKDM2m5INwVt9eZ20r6qeW/Wa15X6bR5HEc7gHC7BgxrU4R4a0AIGCM/wCm/Oo/PivDsfy9aCk8+cwh84nz/GZYzn</latexit><latexit sha1_base64="MK9TkR1XfIASB2dfp9XX0/+fbPE=">AAAB6HicbVBNS8NAEJ3Ur1q/qh69LBbBU0lEqMeiF48t2A9oQ9lsJ+3azSbsboQS+gu8eFDEqz/Jm//GbZuDtj4YeLw3w8y8IBFcG9f9dgobm1vbO8Xd0t7+weFR+fikreNUMWyxWMSqG1CNgktsGW4EdhOFNAoEdoLJ3dzvPKHSPJYPZpqgH9GR5CFn1FipyQblilt1FyDrxMtJBXI0BuWv/jBmaYTSMEG17nluYvyMKsOZwFmpn2pMKJvQEfYslTRC7WeLQ2fkwipDEsbKljRkof6eyGik9TQKbGdEzVivenPxP6+XmvDGz7hMUoOSLReFqSAmJvOvyZArZEZMLaFMcXsrYWOqKDM2m5INwVt9eZ20r6qeW/Wa15X6bR5HEc7gHC7BgxrU4R4a0AIGCM/wCm/Oo/PivDsfy9aCk8+cwh84nz/GZYzn</latexit>

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Page 103: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Statistical Cost ModelUse machine learning to learn a statistical cost model

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Expression AutoTVM Code Generatorc<latexit sha1_base64="MK9TkR1XfIASB2dfp9XX0/+fbPE=">AAAB6HicbVBNS8NAEJ3Ur1q/qh69LBbBU0lEqMeiF48t2A9oQ9lsJ+3azSbsboQS+gu8eFDEqz/Jm//GbZuDtj4YeLw3w8y8IBFcG9f9dgobm1vbO8Xd0t7+weFR+fikreNUMWyxWMSqG1CNgktsGW4EdhOFNAoEdoLJ3dzvPKHSPJYPZpqgH9GR5CFn1FipyQblilt1FyDrxMtJBXI0BuWv/jBmaYTSMEG17nluYvyMKsOZwFmpn2pMKJvQEfYslTRC7WeLQ2fkwipDEsbKljRkof6eyGik9TQKbGdEzVivenPxP6+XmvDGz7hMUoOSLReFqSAmJvOvyZArZEZMLaFMcXsrYWOqKDM2m5INwVt9eZ20r6qeW/Wa15X6bR5HEc7gHC7BgxrU4R4a0AIGCM/wCm/Oo/PivDsfy9aCk8+cwh84nz/GZYzn</latexit><latexit sha1_base64="MK9TkR1XfIASB2dfp9XX0/+fbPE=">AAAB6HicbVBNS8NAEJ3Ur1q/qh69LBbBU0lEqMeiF48t2A9oQ9lsJ+3azSbsboQS+gu8eFDEqz/Jm//GbZuDtj4YeLw3w8y8IBFcG9f9dgobm1vbO8Xd0t7+weFR+fikreNUMWyxWMSqG1CNgktsGW4EdhOFNAoEdoLJ3dzvPKHSPJYPZpqgH9GR5CFn1FipyQblilt1FyDrxMtJBXI0BuWv/jBmaYTSMEG17nluYvyMKsOZwFmpn2pMKJvQEfYslTRC7WeLQ2fkwipDEsbKljRkof6eyGik9TQKbGdEzVivenPxP6+XmvDGz7hMUoOSLReFqSAmJvOvyZArZEZMLaFMcXsrYWOqKDM2m5INwVt9eZ20r6qeW/Wa15X6bR5HEc7gHC7BgxrU4R4a0AIGCM/wCm/Oo/PivDsfy9aCk8+cwh84nz/GZYzn</latexit><latexit sha1_base64="MK9TkR1XfIASB2dfp9XX0/+fbPE=">AAAB6HicbVBNS8NAEJ3Ur1q/qh69LBbBU0lEqMeiF48t2A9oQ9lsJ+3azSbsboQS+gu8eFDEqz/Jm//GbZuDtj4YeLw3w8y8IBFcG9f9dgobm1vbO8Xd0t7+weFR+fikreNUMWyxWMSqG1CNgktsGW4EdhOFNAoEdoLJ3dzvPKHSPJYPZpqgH9GR5CFn1FipyQblilt1FyDrxMtJBXI0BuWv/jBmaYTSMEG17nluYvyMKsOZwFmpn2pMKJvQEfYslTRC7WeLQ2fkwipDEsbKljRkof6eyGik9TQKbGdEzVivenPxP6+XmvDGz7hMUoOSLReFqSAmJvOvyZArZEZMLaFMcXsrYWOqKDM2m5INwVt9eZ20r6qeW/Wa15X6bR5HEc7gHC7BgxrU4R4a0AIGCM/wCm/Oo/PivDsfy9aCk8+cwh84nz/GZYzn</latexit><latexit sha1_base64="MK9TkR1XfIASB2dfp9XX0/+fbPE=">AAAB6HicbVBNS8NAEJ3Ur1q/qh69LBbBU0lEqMeiF48t2A9oQ9lsJ+3azSbsboQS+gu8eFDEqz/Jm//GbZuDtj4YeLw3w8y8IBFcG9f9dgobm1vbO8Xd0t7+weFR+fikreNUMWyxWMSqG1CNgktsGW4EdhOFNAoEdoLJ3dzvPKHSPJYPZpqgH9GR5CFn1FipyQblilt1FyDrxMtJBXI0BuWv/jBmaYTSMEG17nluYvyMKsOZwFmpn2pMKJvQEfYslTRC7WeLQ2fkwipDEsbKljRkof6eyGik9TQKbGdEzVivenPxP6+XmvDGz7hMUoOSLReFqSAmJvOvyZArZEZMLaFMcXsrYWOqKDM2m5INwVt9eZ20r6qeW/Wa15X6bR5HEc7gHC7BgxrU4R4a0AIGCM/wCm/Oo/PivDsfy9aCk8+cwh84nz/GZYzn</latexit>

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Training data

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Page 104: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Statistical Cost ModelUse machine learning to learn a statistical cost model

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Expression AutoTVM Code Generatorc<latexit sha1_base64="MK9TkR1XfIASB2dfp9XX0/+fbPE=">AAAB6HicbVBNS8NAEJ3Ur1q/qh69LBbBU0lEqMeiF48t2A9oQ9lsJ+3azSbsboQS+gu8eFDEqz/Jm//GbZuDtj4YeLw3w8y8IBFcG9f9dgobm1vbO8Xd0t7+weFR+fikreNUMWyxWMSqG1CNgktsGW4EdhOFNAoEdoLJ3dzvPKHSPJYPZpqgH9GR5CFn1FipyQblilt1FyDrxMtJBXI0BuWv/jBmaYTSMEG17nluYvyMKsOZwFmpn2pMKJvQEfYslTRC7WeLQ2fkwipDEsbKljRkof6eyGik9TQKbGdEzVivenPxP6+XmvDGz7hMUoOSLReFqSAmJvOvyZArZEZMLaFMcXsrYWOqKDM2m5INwVt9eZ20r6qeW/Wa15X6bR5HEc7gHC7BgxrU4R4a0AIGCM/wCm/Oo/PivDsfy9aCk8+cwh84nz/GZYzn</latexit><latexit sha1_base64="MK9TkR1XfIASB2dfp9XX0/+fbPE=">AAAB6HicbVBNS8NAEJ3Ur1q/qh69LBbBU0lEqMeiF48t2A9oQ9lsJ+3azSbsboQS+gu8eFDEqz/Jm//GbZuDtj4YeLw3w8y8IBFcG9f9dgobm1vbO8Xd0t7+weFR+fikreNUMWyxWMSqG1CNgktsGW4EdhOFNAoEdoLJ3dzvPKHSPJYPZpqgH9GR5CFn1FipyQblilt1FyDrxMtJBXI0BuWv/jBmaYTSMEG17nluYvyMKsOZwFmpn2pMKJvQEfYslTRC7WeLQ2fkwipDEsbKljRkof6eyGik9TQKbGdEzVivenPxP6+XmvDGz7hMUoOSLReFqSAmJvOvyZArZEZMLaFMcXsrYWOqKDM2m5INwVt9eZ20r6qeW/Wa15X6bR5HEc7gHC7BgxrU4R4a0AIGCM/wCm/Oo/PivDsfy9aCk8+cwh84nz/GZYzn</latexit><latexit sha1_base64="MK9TkR1XfIASB2dfp9XX0/+fbPE=">AAAB6HicbVBNS8NAEJ3Ur1q/qh69LBbBU0lEqMeiF48t2A9oQ9lsJ+3azSbsboQS+gu8eFDEqz/Jm//GbZuDtj4YeLw3w8y8IBFcG9f9dgobm1vbO8Xd0t7+weFR+fikreNUMWyxWMSqG1CNgktsGW4EdhOFNAoEdoLJ3dzvPKHSPJYPZpqgH9GR5CFn1FipyQblilt1FyDrxMtJBXI0BuWv/jBmaYTSMEG17nluYvyMKsOZwFmpn2pMKJvQEfYslTRC7WeLQ2fkwipDEsbKljRkof6eyGik9TQKbGdEzVivenPxP6+XmvDGz7hMUoOSLReFqSAmJvOvyZArZEZMLaFMcXsrYWOqKDM2m5INwVt9eZ20r6qeW/Wa15X6bR5HEc7gHC7BgxrU4R4a0AIGCM/wCm/Oo/PivDsfy9aCk8+cwh84nz/GZYzn</latexit><latexit sha1_base64="MK9TkR1XfIASB2dfp9XX0/+fbPE=">AAAB6HicbVBNS8NAEJ3Ur1q/qh69LBbBU0lEqMeiF48t2A9oQ9lsJ+3azSbsboQS+gu8eFDEqz/Jm//GbZuDtj4YeLw3w8y8IBFcG9f9dgobm1vbO8Xd0t7+weFR+fikreNUMWyxWMSqG1CNgktsGW4EdhOFNAoEdoLJ3dzvPKHSPJYPZpqgH9GR5CFn1FipyQblilt1FyDrxMtJBXI0BuWv/jBmaYTSMEG17nluYvyMKsOZwFmpn2pMKJvQEfYslTRC7WeLQ2fkwipDEsbKljRkof6eyGik9TQKbGdEzVivenPxP6+XmvDGz7hMUoOSLReFqSAmJvOvyZArZEZMLaFMcXsrYWOqKDM2m5INwVt9eZ20r6qeW/Wa15X6bR5HEc7gHC7BgxrU4R4a0AIGCM/wCm/Oo/PivDsfy9aCk8+cwh84nz/GZYzn</latexit>

x

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D<latexit sha1_base64="1Z6CzjBl0OMVztfQ+m452YDkcY0=">AAAB8nicbVDLSsNAFL2pr1pfVZdugkVwVRIRdFnUhcsK9gFtKJPppB06mQkzN0IJ/Qw3LhRx69e482+ctFlo64GBwzn3MueeMBHcoOd9O6W19Y3NrfJ2ZWd3b/+genjUNirVlLWoEkp3Q2KY4JK1kKNg3UQzEoeCdcLJbe53npg2XMlHnCYsiMlI8ohTglbq9WOCY0pEdjcbVGte3ZvDXSV+QWpQoDmofvWHiqYxk0gFMabnewkGGdHIqWCzSj81LCF0QkasZ6kkMTNBNo88c8+sMnQjpe2T6M7V3xsZiY2ZxqGdzCOaZS8X//N6KUbXQcZlkiKTdPFRlAoXlZvf7w65ZhTF1BJCNbdZXTommlC0LVVsCf7yyaukfVH3vbr/cFlr3BR1lOEETuEcfLiCBtxDE1pAQcEzvMKbg86L8+58LEZLTrFzDH/gfP4AdN2RWg==</latexit><latexit sha1_base64="1Z6CzjBl0OMVztfQ+m452YDkcY0=">AAAB8nicbVDLSsNAFL2pr1pfVZdugkVwVRIRdFnUhcsK9gFtKJPppB06mQkzN0IJ/Qw3LhRx69e482+ctFlo64GBwzn3MueeMBHcoOd9O6W19Y3NrfJ2ZWd3b/+genjUNirVlLWoEkp3Q2KY4JK1kKNg3UQzEoeCdcLJbe53npg2XMlHnCYsiMlI8ohTglbq9WOCY0pEdjcbVGte3ZvDXSV+QWpQoDmofvWHiqYxk0gFMabnewkGGdHIqWCzSj81LCF0QkasZ6kkMTNBNo88c8+sMnQjpe2T6M7V3xsZiY2ZxqGdzCOaZS8X//N6KUbXQcZlkiKTdPFRlAoXlZvf7w65ZhTF1BJCNbdZXTommlC0LVVsCf7yyaukfVH3vbr/cFlr3BR1lOEETuEcfLiCBtxDE1pAQcEzvMKbg86L8+58LEZLTrFzDH/gfP4AdN2RWg==</latexit><latexit sha1_base64="1Z6CzjBl0OMVztfQ+m452YDkcY0=">AAAB8nicbVDLSsNAFL2pr1pfVZdugkVwVRIRdFnUhcsK9gFtKJPppB06mQkzN0IJ/Qw3LhRx69e482+ctFlo64GBwzn3MueeMBHcoOd9O6W19Y3NrfJ2ZWd3b/+genjUNirVlLWoEkp3Q2KY4JK1kKNg3UQzEoeCdcLJbe53npg2XMlHnCYsiMlI8ohTglbq9WOCY0pEdjcbVGte3ZvDXSV+QWpQoDmofvWHiqYxk0gFMabnewkGGdHIqWCzSj81LCF0QkasZ6kkMTNBNo88c8+sMnQjpe2T6M7V3xsZiY2ZxqGdzCOaZS8X//N6KUbXQcZlkiKTdPFRlAoXlZvf7w65ZhTF1BJCNbdZXTommlC0LVVsCf7yyaukfVH3vbr/cFlr3BR1lOEETuEcfLiCBtxDE1pAQcEzvMKbg86L8+58LEZLTrFzDH/gfP4AdN2RWg==</latexit><latexit sha1_base64="1Z6CzjBl0OMVztfQ+m452YDkcY0=">AAAB8nicbVDLSsNAFL2pr1pfVZdugkVwVRIRdFnUhcsK9gFtKJPppB06mQkzN0IJ/Qw3LhRx69e482+ctFlo64GBwzn3MueeMBHcoOd9O6W19Y3NrfJ2ZWd3b/+genjUNirVlLWoEkp3Q2KY4JK1kKNg3UQzEoeCdcLJbe53npg2XMlHnCYsiMlI8ohTglbq9WOCY0pEdjcbVGte3ZvDXSV+QWpQoDmofvWHiqYxk0gFMabnewkGGdHIqWCzSj81LCF0QkasZ6kkMTNBNo88c8+sMnQjpe2T6M7V3xsZiY2ZxqGdzCOaZS8X//N6KUbXQcZlkiKTdPFRlAoXlZvf7w65ZhTF1BJCNbdZXTommlC0LVVsCf7yyaukfVH3vbr/cFlr3BR1lOEETuEcfLiCBtxDE1pAQcEzvMKbg86L8+58LEZLTrFzDH/gfP4AdN2RWg==</latexit>

Training data

f(x)<latexit sha1_base64="Appt6dOASLoU0puF9XJna1LvMt4=">AAAB63icbVBNS8NAEJ3Ur1q/qh69LBahXkoigh6LXjxWMG2hDWWz3bRLdzdhdyOW0L/gxYMiXv1D3vw3btoctPXBwOO9GWbmhQln2rjut1NaW9/Y3CpvV3Z29/YPqodHbR2nilCfxDxW3RBrypmkvmGG026iKBYhp51wcpv7nUeqNIvlg5kmNBB4JFnECDa5FNWfzgfVmttw50CrxCtIDQq0BtWv/jAmqaDSEI617nluYoIMK8MIp7NKP9U0wWSCR7RnqcSC6iCb3zpDZ1YZoihWtqRBc/X3RIaF1lMR2k6BzVgve7n4n9dLTXQdZEwmqaGSLBZFKUcmRvnjaMgUJYZPLcFEMXsrImOsMDE2nooNwVt+eZW0Lxqe2/DuL2vNmyKOMpzAKdTBgytowh20wAcCY3iGV3hzhPPivDsfi9aSU8wcwx84nz9sX43R</latexit><latexit sha1_base64="Appt6dOASLoU0puF9XJna1LvMt4=">AAAB63icbVBNS8NAEJ3Ur1q/qh69LBahXkoigh6LXjxWMG2hDWWz3bRLdzdhdyOW0L/gxYMiXv1D3vw3btoctPXBwOO9GWbmhQln2rjut1NaW9/Y3CpvV3Z29/YPqodHbR2nilCfxDxW3RBrypmkvmGG026iKBYhp51wcpv7nUeqNIvlg5kmNBB4JFnECDa5FNWfzgfVmttw50CrxCtIDQq0BtWv/jAmqaDSEI617nluYoIMK8MIp7NKP9U0wWSCR7RnqcSC6iCb3zpDZ1YZoihWtqRBc/X3RIaF1lMR2k6BzVgve7n4n9dLTXQdZEwmqaGSLBZFKUcmRvnjaMgUJYZPLcFEMXsrImOsMDE2nooNwVt+eZW0Lxqe2/DuL2vNmyKOMpzAKdTBgytowh20wAcCY3iGV3hzhPPivDsfi9aSU8wcwx84nz9sX43R</latexit><latexit sha1_base64="Appt6dOASLoU0puF9XJna1LvMt4=">AAAB63icbVBNS8NAEJ3Ur1q/qh69LBahXkoigh6LXjxWMG2hDWWz3bRLdzdhdyOW0L/gxYMiXv1D3vw3btoctPXBwOO9GWbmhQln2rjut1NaW9/Y3CpvV3Z29/YPqodHbR2nilCfxDxW3RBrypmkvmGG026iKBYhp51wcpv7nUeqNIvlg5kmNBB4JFnECDa5FNWfzgfVmttw50CrxCtIDQq0BtWv/jAmqaDSEI617nluYoIMK8MIp7NKP9U0wWSCR7RnqcSC6iCb3zpDZ1YZoihWtqRBc/X3RIaF1lMR2k6BzVgve7n4n9dLTXQdZEwmqaGSLBZFKUcmRvnjaMgUJYZPLcFEMXsrImOsMDE2nooNwVt+eZW0Lxqe2/DuL2vNmyKOMpzAKdTBgytowh20wAcCY3iGV3hzhPPivDsfi9aSU8wcwx84nz9sX43R</latexit><latexit sha1_base64="Appt6dOASLoU0puF9XJna1LvMt4=">AAAB63icbVBNS8NAEJ3Ur1q/qh69LBahXkoigh6LXjxWMG2hDWWz3bRLdzdhdyOW0L/gxYMiXv1D3vw3btoctPXBwOO9GWbmhQln2rjut1NaW9/Y3CpvV3Z29/YPqodHbR2nilCfxDxW3RBrypmkvmGG026iKBYhp51wcpv7nUeqNIvlg5kmNBB4JFnECDa5FNWfzgfVmttw50CrxCtIDQq0BtWv/jAmqaDSEI617nluYoIMK8MIp7NKP9U0wWSCR7RnqcSC6iCb3zpDZ1YZoihWtqRBc/X3RIaF1lMR2k6BzVgve7n4n9dLTXQdZEwmqaGSLBZFKUcmRvnjaMgUJYZPLcFEMXsrImOsMDE2nooNwVt+eZW0Lxqe2/DuL2vNmyKOMpzAKdTBgytowh20wAcCY3iGV3hzhPPivDsfi9aSU8wcwx84nz9sX43R</latexit>

Statistical Cost Model

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learningf̂(e, c)

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Page 105: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Statistical Cost ModelUse machine learning to learn a statistical cost model

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Search Space

Expression AutoTVM Code Generatorc<latexit sha1_base64="MK9TkR1XfIASB2dfp9XX0/+fbPE=">AAAB6HicbVBNS8NAEJ3Ur1q/qh69LBbBU0lEqMeiF48t2A9oQ9lsJ+3azSbsboQS+gu8eFDEqz/Jm//GbZuDtj4YeLw3w8y8IBFcG9f9dgobm1vbO8Xd0t7+weFR+fikreNUMWyxWMSqG1CNgktsGW4EdhOFNAoEdoLJ3dzvPKHSPJYPZpqgH9GR5CFn1FipyQblilt1FyDrxMtJBXI0BuWv/jBmaYTSMEG17nluYvyMKsOZwFmpn2pMKJvQEfYslTRC7WeLQ2fkwipDEsbKljRkof6eyGik9TQKbGdEzVivenPxP6+XmvDGz7hMUoOSLReFqSAmJvOvyZArZEZMLaFMcXsrYWOqKDM2m5INwVt9eZ20r6qeW/Wa15X6bR5HEc7gHC7BgxrU4R4a0AIGCM/wCm/Oo/PivDsfy9aCk8+cwh84nz/GZYzn</latexit><latexit sha1_base64="MK9TkR1XfIASB2dfp9XX0/+fbPE=">AAAB6HicbVBNS8NAEJ3Ur1q/qh69LBbBU0lEqMeiF48t2A9oQ9lsJ+3azSbsboQS+gu8eFDEqz/Jm//GbZuDtj4YeLw3w8y8IBFcG9f9dgobm1vbO8Xd0t7+weFR+fikreNUMWyxWMSqG1CNgktsGW4EdhOFNAoEdoLJ3dzvPKHSPJYPZpqgH9GR5CFn1FipyQblilt1FyDrxMtJBXI0BuWv/jBmaYTSMEG17nluYvyMKsOZwFmpn2pMKJvQEfYslTRC7WeLQ2fkwipDEsbKljRkof6eyGik9TQKbGdEzVivenPxP6+XmvDGz7hMUoOSLReFqSAmJvOvyZArZEZMLaFMcXsrYWOqKDM2m5INwVt9eZ20r6qeW/Wa15X6bR5HEc7gHC7BgxrU4R4a0AIGCM/wCm/Oo/PivDsfy9aCk8+cwh84nz/GZYzn</latexit><latexit sha1_base64="MK9TkR1XfIASB2dfp9XX0/+fbPE=">AAAB6HicbVBNS8NAEJ3Ur1q/qh69LBbBU0lEqMeiF48t2A9oQ9lsJ+3azSbsboQS+gu8eFDEqz/Jm//GbZuDtj4YeLw3w8y8IBFcG9f9dgobm1vbO8Xd0t7+weFR+fikreNUMWyxWMSqG1CNgktsGW4EdhOFNAoEdoLJ3dzvPKHSPJYPZpqgH9GR5CFn1FipyQblilt1FyDrxMtJBXI0BuWv/jBmaYTSMEG17nluYvyMKsOZwFmpn2pMKJvQEfYslTRC7WeLQ2fkwipDEsbKljRkof6eyGik9TQKbGdEzVivenPxP6+XmvDGz7hMUoOSLReFqSAmJvOvyZArZEZMLaFMcXsrYWOqKDM2m5INwVt9eZ20r6qeW/Wa15X6bR5HEc7gHC7BgxrU4R4a0AIGCM/wCm/Oo/PivDsfy9aCk8+cwh84nz/GZYzn</latexit><latexit sha1_base64="MK9TkR1XfIASB2dfp9XX0/+fbPE=">AAAB6HicbVBNS8NAEJ3Ur1q/qh69LBbBU0lEqMeiF48t2A9oQ9lsJ+3azSbsboQS+gu8eFDEqz/Jm//GbZuDtj4YeLw3w8y8IBFcG9f9dgobm1vbO8Xd0t7+weFR+fikreNUMWyxWMSqG1CNgktsGW4EdhOFNAoEdoLJ3dzvPKHSPJYPZpqgH9GR5CFn1FipyQblilt1FyDrxMtJBXI0BuWv/jBmaYTSMEG17nluYvyMKsOZwFmpn2pMKJvQEfYslTRC7WeLQ2fkwipDEsbKljRkof6eyGik9TQKbGdEzVivenPxP6+XmvDGz7hMUoOSLReFqSAmJvOvyZArZEZMLaFMcXsrYWOqKDM2m5INwVt9eZ20r6qeW/Wa15X6bR5HEc7gHC7BgxrU4R4a0AIGCM/wCm/Oo/PivDsfy9aCk8+cwh84nz/GZYzn</latexit>

x

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Training data

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Benefit: Automatically adapt to hardware type

Statistical Cost Model

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learningf̂(e, c)

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Page 106: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Statistical Cost ModelUse machine learning to learn a statistical cost model

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Expression AutoTVM Code Generatorc<latexit sha1_base64="MK9TkR1XfIASB2dfp9XX0/+fbPE=">AAAB6HicbVBNS8NAEJ3Ur1q/qh69LBbBU0lEqMeiF48t2A9oQ9lsJ+3azSbsboQS+gu8eFDEqz/Jm//GbZuDtj4YeLw3w8y8IBFcG9f9dgobm1vbO8Xd0t7+weFR+fikreNUMWyxWMSqG1CNgktsGW4EdhOFNAoEdoLJ3dzvPKHSPJYPZpqgH9GR5CFn1FipyQblilt1FyDrxMtJBXI0BuWv/jBmaYTSMEG17nluYvyMKsOZwFmpn2pMKJvQEfYslTRC7WeLQ2fkwipDEsbKljRkof6eyGik9TQKbGdEzVivenPxP6+XmvDGz7hMUoOSLReFqSAmJvOvyZArZEZMLaFMcXsrYWOqKDM2m5INwVt9eZ20r6qeW/Wa15X6bR5HEc7gHC7BgxrU4R4a0AIGCM/wCm/Oo/PivDsfy9aCk8+cwh84nz/GZYzn</latexit><latexit sha1_base64="MK9TkR1XfIASB2dfp9XX0/+fbPE=">AAAB6HicbVBNS8NAEJ3Ur1q/qh69LBbBU0lEqMeiF48t2A9oQ9lsJ+3azSbsboQS+gu8eFDEqz/Jm//GbZuDtj4YeLw3w8y8IBFcG9f9dgobm1vbO8Xd0t7+weFR+fikreNUMWyxWMSqG1CNgktsGW4EdhOFNAoEdoLJ3dzvPKHSPJYPZpqgH9GR5CFn1FipyQblilt1FyDrxMtJBXI0BuWv/jBmaYTSMEG17nluYvyMKsOZwFmpn2pMKJvQEfYslTRC7WeLQ2fkwipDEsbKljRkof6eyGik9TQKbGdEzVivenPxP6+XmvDGz7hMUoOSLReFqSAmJvOvyZArZEZMLaFMcXsrYWOqKDM2m5INwVt9eZ20r6qeW/Wa15X6bR5HEc7gHC7BgxrU4R4a0AIGCM/wCm/Oo/PivDsfy9aCk8+cwh84nz/GZYzn</latexit><latexit sha1_base64="MK9TkR1XfIASB2dfp9XX0/+fbPE=">AAAB6HicbVBNS8NAEJ3Ur1q/qh69LBbBU0lEqMeiF48t2A9oQ9lsJ+3azSbsboQS+gu8eFDEqz/Jm//GbZuDtj4YeLw3w8y8IBFcG9f9dgobm1vbO8Xd0t7+weFR+fikreNUMWyxWMSqG1CNgktsGW4EdhOFNAoEdoLJ3dzvPKHSPJYPZpqgH9GR5CFn1FipyQblilt1FyDrxMtJBXI0BuWv/jBmaYTSMEG17nluYvyMKsOZwFmpn2pMKJvQEfYslTRC7WeLQ2fkwipDEsbKljRkof6eyGik9TQKbGdEzVivenPxP6+XmvDGz7hMUoOSLReFqSAmJvOvyZArZEZMLaFMcXsrYWOqKDM2m5INwVt9eZ20r6qeW/Wa15X6bR5HEc7gHC7BgxrU4R4a0AIGCM/wCm/Oo/PivDsfy9aCk8+cwh84nz/GZYzn</latexit><latexit sha1_base64="MK9TkR1XfIASB2dfp9XX0/+fbPE=">AAAB6HicbVBNS8NAEJ3Ur1q/qh69LBbBU0lEqMeiF48t2A9oQ9lsJ+3azSbsboQS+gu8eFDEqz/Jm//GbZuDtj4YeLw3w8y8IBFcG9f9dgobm1vbO8Xd0t7+weFR+fikreNUMWyxWMSqG1CNgktsGW4EdhOFNAoEdoLJ3dzvPKHSPJYPZpqgH9GR5CFn1FipyQblilt1FyDrxMtJBXI0BuWv/jBmaYTSMEG17nluYvyMKsOZwFmpn2pMKJvQEfYslTRC7WeLQ2fkwipDEsbKljRkof6eyGik9TQKbGdEzVivenPxP6+XmvDGz7hMUoOSLReFqSAmJvOvyZArZEZMLaFMcXsrYWOqKDM2m5INwVt9eZ20r6qeW/Wa15X6bR5HEc7gHC7BgxrU4R4a0AIGCM/wCm/Oo/PivDsfy9aCk8+cwh84nz/GZYzn</latexit>

x

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D<latexit sha1_base64="1Z6CzjBl0OMVztfQ+m452YDkcY0=">AAAB8nicbVDLSsNAFL2pr1pfVZdugkVwVRIRdFnUhcsK9gFtKJPppB06mQkzN0IJ/Qw3LhRx69e482+ctFlo64GBwzn3MueeMBHcoOd9O6W19Y3NrfJ2ZWd3b/+genjUNirVlLWoEkp3Q2KY4JK1kKNg3UQzEoeCdcLJbe53npg2XMlHnCYsiMlI8ohTglbq9WOCY0pEdjcbVGte3ZvDXSV+QWpQoDmofvWHiqYxk0gFMabnewkGGdHIqWCzSj81LCF0QkasZ6kkMTNBNo88c8+sMnQjpe2T6M7V3xsZiY2ZxqGdzCOaZS8X//N6KUbXQcZlkiKTdPFRlAoXlZvf7w65ZhTF1BJCNbdZXTommlC0LVVsCf7yyaukfVH3vbr/cFlr3BR1lOEETuEcfLiCBtxDE1pAQcEzvMKbg86L8+58LEZLTrFzDH/gfP4AdN2RWg==</latexit><latexit sha1_base64="1Z6CzjBl0OMVztfQ+m452YDkcY0=">AAAB8nicbVDLSsNAFL2pr1pfVZdugkVwVRIRdFnUhcsK9gFtKJPppB06mQkzN0IJ/Qw3LhRx69e482+ctFlo64GBwzn3MueeMBHcoOd9O6W19Y3NrfJ2ZWd3b/+genjUNirVlLWoEkp3Q2KY4JK1kKNg3UQzEoeCdcLJbe53npg2XMlHnCYsiMlI8ohTglbq9WOCY0pEdjcbVGte3ZvDXSV+QWpQoDmofvWHiqYxk0gFMabnewkGGdHIqWCzSj81LCF0QkasZ6kkMTNBNo88c8+sMnQjpe2T6M7V3xsZiY2ZxqGdzCOaZS8X//N6KUbXQcZlkiKTdPFRlAoXlZvf7w65ZhTF1BJCNbdZXTommlC0LVVsCf7yyaukfVH3vbr/cFlr3BR1lOEETuEcfLiCBtxDE1pAQcEzvMKbg86L8+58LEZLTrFzDH/gfP4AdN2RWg==</latexit><latexit sha1_base64="1Z6CzjBl0OMVztfQ+m452YDkcY0=">AAAB8nicbVDLSsNAFL2pr1pfVZdugkVwVRIRdFnUhcsK9gFtKJPppB06mQkzN0IJ/Qw3LhRx69e482+ctFlo64GBwzn3MueeMBHcoOd9O6W19Y3NrfJ2ZWd3b/+genjUNirVlLWoEkp3Q2KY4JK1kKNg3UQzEoeCdcLJbe53npg2XMlHnCYsiMlI8ohTglbq9WOCY0pEdjcbVGte3ZvDXSV+QWpQoDmofvWHiqYxk0gFMabnewkGGdHIqWCzSj81LCF0QkasZ6kkMTNBNo88c8+sMnQjpe2T6M7V3xsZiY2ZxqGdzCOaZS8X//N6KUbXQcZlkiKTdPFRlAoXlZvf7w65ZhTF1BJCNbdZXTommlC0LVVsCf7yyaukfVH3vbr/cFlr3BR1lOEETuEcfLiCBtxDE1pAQcEzvMKbg86L8+58LEZLTrFzDH/gfP4AdN2RWg==</latexit><latexit sha1_base64="1Z6CzjBl0OMVztfQ+m452YDkcY0=">AAAB8nicbVDLSsNAFL2pr1pfVZdugkVwVRIRdFnUhcsK9gFtKJPppB06mQkzN0IJ/Qw3LhRx69e482+ctFlo64GBwzn3MueeMBHcoOd9O6W19Y3NrfJ2ZWd3b/+genjUNirVlLWoEkp3Q2KY4JK1kKNg3UQzEoeCdcLJbe53npg2XMlHnCYsiMlI8ohTglbq9WOCY0pEdjcbVGte3ZvDXSV+QWpQoDmofvWHiqYxk0gFMabnewkGGdHIqWCzSj81LCF0QkasZ6kkMTNBNo88c8+sMnQjpe2T6M7V3xsZiY2ZxqGdzCOaZS8X//N6KUbXQcZlkiKTdPFRlAoXlZvf7w65ZhTF1BJCNbdZXTommlC0LVVsCf7yyaukfVH3vbr/cFlr3BR1lOEETuEcfLiCBtxDE1pAQcEzvMKbg86L8+58LEZLTrFzDH/gfP4AdN2RWg==</latexit>

Training data

f(x)<latexit sha1_base64="Appt6dOASLoU0puF9XJna1LvMt4=">AAAB63icbVBNS8NAEJ3Ur1q/qh69LBahXkoigh6LXjxWMG2hDWWz3bRLdzdhdyOW0L/gxYMiXv1D3vw3btoctPXBwOO9GWbmhQln2rjut1NaW9/Y3CpvV3Z29/YPqodHbR2nilCfxDxW3RBrypmkvmGG026iKBYhp51wcpv7nUeqNIvlg5kmNBB4JFnECDa5FNWfzgfVmttw50CrxCtIDQq0BtWv/jAmqaDSEI617nluYoIMK8MIp7NKP9U0wWSCR7RnqcSC6iCb3zpDZ1YZoihWtqRBc/X3RIaF1lMR2k6BzVgve7n4n9dLTXQdZEwmqaGSLBZFKUcmRvnjaMgUJYZPLcFEMXsrImOsMDE2nooNwVt+eZW0Lxqe2/DuL2vNmyKOMpzAKdTBgytowh20wAcCY3iGV3hzhPPivDsfi9aSU8wcwx84nz9sX43R</latexit><latexit sha1_base64="Appt6dOASLoU0puF9XJna1LvMt4=">AAAB63icbVBNS8NAEJ3Ur1q/qh69LBahXkoigh6LXjxWMG2hDWWz3bRLdzdhdyOW0L/gxYMiXv1D3vw3btoctPXBwOO9GWbmhQln2rjut1NaW9/Y3CpvV3Z29/YPqodHbR2nilCfxDxW3RBrypmkvmGG026iKBYhp51wcpv7nUeqNIvlg5kmNBB4JFnECDa5FNWfzgfVmttw50CrxCtIDQq0BtWv/jAmqaDSEI617nluYoIMK8MIp7NKP9U0wWSCR7RnqcSC6iCb3zpDZ1YZoihWtqRBc/X3RIaF1lMR2k6BzVgve7n4n9dLTXQdZEwmqaGSLBZFKUcmRvnjaMgUJYZPLcFEMXsrImOsMDE2nooNwVt+eZW0Lxqe2/DuL2vNmyKOMpzAKdTBgytowh20wAcCY3iGV3hzhPPivDsfi9aSU8wcwx84nz9sX43R</latexit><latexit sha1_base64="Appt6dOASLoU0puF9XJna1LvMt4=">AAAB63icbVBNS8NAEJ3Ur1q/qh69LBahXkoigh6LXjxWMG2hDWWz3bRLdzdhdyOW0L/gxYMiXv1D3vw3btoctPXBwOO9GWbmhQln2rjut1NaW9/Y3CpvV3Z29/YPqodHbR2nilCfxDxW3RBrypmkvmGG026iKBYhp51wcpv7nUeqNIvlg5kmNBB4JFnECDa5FNWfzgfVmttw50CrxCtIDQq0BtWv/jAmqaDSEI617nluYoIMK8MIp7NKP9U0wWSCR7RnqcSC6iCb3zpDZ1YZoihWtqRBc/X3RIaF1lMR2k6BzVgve7n4n9dLTXQdZEwmqaGSLBZFKUcmRvnjaMgUJYZPLcFEMXsrImOsMDE2nooNwVt+eZW0Lxqe2/DuL2vNmyKOMpzAKdTBgytowh20wAcCY3iGV3hzhPPivDsfi9aSU8wcwx84nz9sX43R</latexit><latexit sha1_base64="Appt6dOASLoU0puF9XJna1LvMt4=">AAAB63icbVBNS8NAEJ3Ur1q/qh69LBahXkoigh6LXjxWMG2hDWWz3bRLdzdhdyOW0L/gxYMiXv1D3vw3btoctPXBwOO9GWbmhQln2rjut1NaW9/Y3CpvV3Z29/YPqodHbR2nilCfxDxW3RBrypmkvmGG026iKBYhp51wcpv7nUeqNIvlg5kmNBB4JFnECDa5FNWfzgfVmttw50CrxCtIDQq0BtWv/jAmqaDSEI617nluYoIMK8MIp7NKP9U0wWSCR7RnqcSC6iCb3zpDZ1YZoihWtqRBc/X3RIaF1lMR2k6BzVgve7n4n9dLTXQdZEwmqaGSLBZFKUcmRvnjaMgUJYZPLcFEMXsrImOsMDE2nooNwVt+eZW0Lxqe2/DuL2vNmyKOMpzAKdTBgytowh20wAcCY3iGV3hzhPPivDsfi9aSU8wcwx84nz9sX43R</latexit>

Benefit: Automatically adapt to hardware type

Statistical Cost Model

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learningf̂(e, c)

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Challenge: How to design the cost model

Page 107: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Unique Problem Characteristics

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Search Space

Expression AutoTVM Code Generatorc

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x

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Training data

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Statistical Cost Model

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learningf̂(e, c)

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Page 108: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Unique Problem Characteristics

Relatively low experiment cost

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Expression AutoTVM Code Generatorc

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x

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D<latexit sha1_base64="1Z6CzjBl0OMVztfQ+m452YDkcY0=">AAAB8nicbVDLSsNAFL2pr1pfVZdugkVwVRIRdFnUhcsK9gFtKJPppB06mQkzN0IJ/Qw3LhRx69e482+ctFlo64GBwzn3MueeMBHcoOd9O6W19Y3NrfJ2ZWd3b/+genjUNirVlLWoEkp3Q2KY4JK1kKNg3UQzEoeCdcLJbe53npg2XMlHnCYsiMlI8ohTglbq9WOCY0pEdjcbVGte3ZvDXSV+QWpQoDmofvWHiqYxk0gFMabnewkGGdHIqWCzSj81LCF0QkasZ6kkMTNBNo88c8+sMnQjpe2T6M7V3xsZiY2ZxqGdzCOaZS8X//N6KUbXQcZlkiKTdPFRlAoXlZvf7w65ZhTF1BJCNbdZXTommlC0LVVsCf7yyaukfVH3vbr/cFlr3BR1lOEETuEcfLiCBtxDE1pAQcEzvMKbg86L8+58LEZLTrFzDH/gfP4AdN2RWg==</latexit><latexit sha1_base64="1Z6CzjBl0OMVztfQ+m452YDkcY0=">AAAB8nicbVDLSsNAFL2pr1pfVZdugkVwVRIRdFnUhcsK9gFtKJPppB06mQkzN0IJ/Qw3LhRx69e482+ctFlo64GBwzn3MueeMBHcoOd9O6W19Y3NrfJ2ZWd3b/+genjUNirVlLWoEkp3Q2KY4JK1kKNg3UQzEoeCdcLJbe53npg2XMlHnCYsiMlI8ohTglbq9WOCY0pEdjcbVGte3ZvDXSV+QWpQoDmofvWHiqYxk0gFMabnewkGGdHIqWCzSj81LCF0QkasZ6kkMTNBNo88c8+sMnQjpe2T6M7V3xsZiY2ZxqGdzCOaZS8X//N6KUbXQcZlkiKTdPFRlAoXlZvf7w65ZhTF1BJCNbdZXTommlC0LVVsCf7yyaukfVH3vbr/cFlr3BR1lOEETuEcfLiCBtxDE1pAQcEzvMKbg86L8+58LEZLTrFzDH/gfP4AdN2RWg==</latexit><latexit sha1_base64="1Z6CzjBl0OMVztfQ+m452YDkcY0=">AAAB8nicbVDLSsNAFL2pr1pfVZdugkVwVRIRdFnUhcsK9gFtKJPppB06mQkzN0IJ/Qw3LhRx69e482+ctFlo64GBwzn3MueeMBHcoOd9O6W19Y3NrfJ2ZWd3b/+genjUNirVlLWoEkp3Q2KY4JK1kKNg3UQzEoeCdcLJbe53npg2XMlHnCYsiMlI8ohTglbq9WOCY0pEdjcbVGte3ZvDXSV+QWpQoDmofvWHiqYxk0gFMabnewkGGdHIqWCzSj81LCF0QkasZ6kkMTNBNo88c8+sMnQjpe2T6M7V3xsZiY2ZxqGdzCOaZS8X//N6KUbXQcZlkiKTdPFRlAoXlZvf7w65ZhTF1BJCNbdZXTommlC0LVVsCf7yyaukfVH3vbr/cFlr3BR1lOEETuEcfLiCBtxDE1pAQcEzvMKbg86L8+58LEZLTrFzDH/gfP4AdN2RWg==</latexit><latexit sha1_base64="1Z6CzjBl0OMVztfQ+m452YDkcY0=">AAAB8nicbVDLSsNAFL2pr1pfVZdugkVwVRIRdFnUhcsK9gFtKJPppB06mQkzN0IJ/Qw3LhRx69e482+ctFlo64GBwzn3MueeMBHcoOd9O6W19Y3NrfJ2ZWd3b/+genjUNirVlLWoEkp3Q2KY4JK1kKNg3UQzEoeCdcLJbe53npg2XMlHnCYsiMlI8ohTglbq9WOCY0pEdjcbVGte3ZvDXSV+QWpQoDmofvWHiqYxk0gFMabnewkGGdHIqWCzSj81LCF0QkasZ6kkMTNBNo88c8+sMnQjpe2T6M7V3xsZiY2ZxqGdzCOaZS8X//N6KUbXQcZlkiKTdPFRlAoXlZvf7w65ZhTF1BJCNbdZXTommlC0LVVsCf7yyaukfVH3vbr/cFlr3BR1lOEETuEcfLiCBtxDE1pAQcEzvMKbg86L8+58LEZLTrFzDH/gfP4AdN2RWg==</latexit>

Training data

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Statistical Cost Model

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learningf̂(e, c)

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Page 109: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Unique Problem Characteristics

Relatively low experiment cost

Program-aware modeling

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Search Space

Expression AutoTVM Code Generatorc

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x

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Training data

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Statistical Cost Model

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learningf̂(e, c)

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Page 110: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Unique Problem Characteristics

Large number of similar tasks

Relatively low experiment cost

Program-aware modeling

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Expression AutoTVM Code Generatorc

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x

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D<latexit sha1_base64="1Z6CzjBl0OMVztfQ+m452YDkcY0=">AAAB8nicbVDLSsNAFL2pr1pfVZdugkVwVRIRdFnUhcsK9gFtKJPppB06mQkzN0IJ/Qw3LhRx69e482+ctFlo64GBwzn3MueeMBHcoOd9O6W19Y3NrfJ2ZWd3b/+genjUNirVlLWoEkp3Q2KY4JK1kKNg3UQzEoeCdcLJbe53npg2XMlHnCYsiMlI8ohTglbq9WOCY0pEdjcbVGte3ZvDXSV+QWpQoDmofvWHiqYxk0gFMabnewkGGdHIqWCzSj81LCF0QkasZ6kkMTNBNo88c8+sMnQjpe2T6M7V3xsZiY2ZxqGdzCOaZS8X//N6KUbXQcZlkiKTdPFRlAoXlZvf7w65ZhTF1BJCNbdZXTommlC0LVVsCf7yyaukfVH3vbr/cFlr3BR1lOEETuEcfLiCBtxDE1pAQcEzvMKbg86L8+58LEZLTrFzDH/gfP4AdN2RWg==</latexit><latexit sha1_base64="1Z6CzjBl0OMVztfQ+m452YDkcY0=">AAAB8nicbVDLSsNAFL2pr1pfVZdugkVwVRIRdFnUhcsK9gFtKJPppB06mQkzN0IJ/Qw3LhRx69e482+ctFlo64GBwzn3MueeMBHcoOd9O6W19Y3NrfJ2ZWd3b/+genjUNirVlLWoEkp3Q2KY4JK1kKNg3UQzEoeCdcLJbe53npg2XMlHnCYsiMlI8ohTglbq9WOCY0pEdjcbVGte3ZvDXSV+QWpQoDmofvWHiqYxk0gFMabnewkGGdHIqWCzSj81LCF0QkasZ6kkMTNBNo88c8+sMnQjpe2T6M7V3xsZiY2ZxqGdzCOaZS8X//N6KUbXQcZlkiKTdPFRlAoXlZvf7w65ZhTF1BJCNbdZXTommlC0LVVsCf7yyaukfVH3vbr/cFlr3BR1lOEETuEcfLiCBtxDE1pAQcEzvMKbg86L8+58LEZLTrFzDH/gfP4AdN2RWg==</latexit><latexit sha1_base64="1Z6CzjBl0OMVztfQ+m452YDkcY0=">AAAB8nicbVDLSsNAFL2pr1pfVZdugkVwVRIRdFnUhcsK9gFtKJPppB06mQkzN0IJ/Qw3LhRx69e482+ctFlo64GBwzn3MueeMBHcoOd9O6W19Y3NrfJ2ZWd3b/+genjUNirVlLWoEkp3Q2KY4JK1kKNg3UQzEoeCdcLJbe53npg2XMlHnCYsiMlI8ohTglbq9WOCY0pEdjcbVGte3ZvDXSV+QWpQoDmofvWHiqYxk0gFMabnewkGGdHIqWCzSj81LCF0QkasZ6kkMTNBNo88c8+sMnQjpe2T6M7V3xsZiY2ZxqGdzCOaZS8X//N6KUbXQcZlkiKTdPFRlAoXlZvf7w65ZhTF1BJCNbdZXTommlC0LVVsCf7yyaukfVH3vbr/cFlr3BR1lOEETuEcfLiCBtxDE1pAQcEzvMKbg86L8+58LEZLTrFzDH/gfP4AdN2RWg==</latexit><latexit sha1_base64="1Z6CzjBl0OMVztfQ+m452YDkcY0=">AAAB8nicbVDLSsNAFL2pr1pfVZdugkVwVRIRdFnUhcsK9gFtKJPppB06mQkzN0IJ/Qw3LhRx69e482+ctFlo64GBwzn3MueeMBHcoOd9O6W19Y3NrfJ2ZWd3b/+genjUNirVlLWoEkp3Q2KY4JK1kKNg3UQzEoeCdcLJbe53npg2XMlHnCYsiMlI8ohTglbq9WOCY0pEdjcbVGte3ZvDXSV+QWpQoDmofvWHiqYxk0gFMabnewkGGdHIqWCzSj81LCF0QkasZ6kkMTNBNo88c8+sMnQjpe2T6M7V3xsZiY2ZxqGdzCOaZS8X//N6KUbXQcZlkiKTdPFRlAoXlZvf7w65ZhTF1BJCNbdZXTommlC0LVVsCf7yyaukfVH3vbr/cFlr3BR1lOEETuEcfLiCBtxDE1pAQcEzvMKbg86L8+58LEZLTrFzDH/gfP4AdN2RWg==</latexit>

Training data

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Statistical Cost Model

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learningf̂(e, c)

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Page 111: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Unique Problem Characteristics

Large number of similar tasks

Relatively low experiment cost

Program-aware modeling

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Search Space

Expression AutoTVM Code Generatorc

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x

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Training data

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Statistical Cost Model

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learningf̂(e, c)

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Vanilla Cost ModelingHigh-level

Configurationsc<latexit sha1_base64="MK9TkR1XfIASB2dfp9XX0/+fbPE=">AAAB6HicbVBNS8NAEJ3Ur1q/qh69LBbBU0lEqMeiF48t2A9oQ9lsJ+3azSbsboQS+gu8eFDEqz/Jm//GbZuDtj4YeLw3w8y8IBFcG9f9dgobm1vbO8Xd0t7+weFR+fikreNUMWyxWMSqG1CNgktsGW4EdhOFNAoEdoLJ3dzvPKHSPJYPZpqgH9GR5CFn1FipyQblilt1FyDrxMtJBXI0BuWv/jBmaYTSMEG17nluYvyMKsOZwFmpn2pMKJvQEfYslTRC7WeLQ2fkwipDEsbKljRkof6eyGik9TQKbGdEzVivenPxP6+XmvDGz7hMUoOSLReFqSAmJvOvyZArZEZMLaFMcXsrYWOqKDM2m5INwVt9eZ20r6qeW/Wa15X6bR5HEc7gHC7BgxrU4R4a0AIGCM/wCm/Oo/PivDsfy9aCk8+cwh84nz/GZYzn</latexit><latexit sha1_base64="MK9TkR1XfIASB2dfp9XX0/+fbPE=">AAAB6HicbVBNS8NAEJ3Ur1q/qh69LBbBU0lEqMeiF48t2A9oQ9lsJ+3azSbsboQS+gu8eFDEqz/Jm//GbZuDtj4YeLw3w8y8IBFcG9f9dgobm1vbO8Xd0t7+weFR+fikreNUMWyxWMSqG1CNgktsGW4EdhOFNAoEdoLJ3dzvPKHSPJYPZpqgH9GR5CFn1FipyQblilt1FyDrxMtJBXI0BuWv/jBmaYTSMEG17nluYvyMKsOZwFmpn2pMKJvQEfYslTRC7WeLQ2fkwipDEsbKljRkof6eyGik9TQKbGdEzVivenPxP6+XmvDGz7hMUoOSLReFqSAmJvOvyZArZEZMLaFMcXsrYWOqKDM2m5INwVt9eZ20r6qeW/Wa15X6bR5HEc7gHC7BgxrU4R4a0AIGCM/wCm/Oo/PivDsfy9aCk8+cwh84nz/GZYzn</latexit><latexit sha1_base64="MK9TkR1XfIASB2dfp9XX0/+fbPE=">AAAB6HicbVBNS8NAEJ3Ur1q/qh69LBbBU0lEqMeiF48t2A9oQ9lsJ+3azSbsboQS+gu8eFDEqz/Jm//GbZuDtj4YeLw3w8y8IBFcG9f9dgobm1vbO8Xd0t7+weFR+fikreNUMWyxWMSqG1CNgktsGW4EdhOFNAoEdoLJ3dzvPKHSPJYPZpqgH9GR5CFn1FipyQblilt1FyDrxMtJBXI0BuWv/jBmaYTSMEG17nluYvyMKsOZwFmpn2pMKJvQEfYslTRC7WeLQ2fkwipDEsbKljRkof6eyGik9TQKbGdEzVivenPxP6+XmvDGz7hMUoOSLReFqSAmJvOvyZArZEZMLaFMcXsrYWOqKDM2m5INwVt9eZ20r6qeW/Wa15X6bR5HEc7gHC7BgxrU4R4a0AIGCM/wCm/Oo/PivDsfy9aCk8+cwh84nz/GZYzn</latexit><latexit sha1_base64="MK9TkR1XfIASB2dfp9XX0/+fbPE=">AAAB6HicbVBNS8NAEJ3Ur1q/qh69LBbBU0lEqMeiF48t2A9oQ9lsJ+3azSbsboQS+gu8eFDEqz/Jm//GbZuDtj4YeLw3w8y8IBFcG9f9dgobm1vbO8Xd0t7+weFR+fikreNUMWyxWMSqG1CNgktsGW4EdhOFNAoEdoLJ3dzvPKHSPJYPZpqgH9GR5CFn1FipyQblilt1FyDrxMtJBXI0BuWv/jBmaYTSMEG17nluYvyMKsOZwFmpn2pMKJvQEfYslTRC7WeLQ2fkwipDEsbKljRkof6eyGik9TQKbGdEzVivenPxP6+XmvDGz7hMUoOSLReFqSAmJvOvyZArZEZMLaFMcXsrYWOqKDM2m5INwVt9eZ20r6qeW/Wa15X6bR5HEc7gHC7BgxrU4R4a0AIGCM/wCm/Oo/PivDsfy9aCk8+cwh84nz/GZYzn</latexit>

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Vanilla Cost ModelingHigh-level

Configurationsc<latexit sha1_base64="MK9TkR1XfIASB2dfp9XX0/+fbPE=">AAAB6HicbVBNS8NAEJ3Ur1q/qh69LBbBU0lEqMeiF48t2A9oQ9lsJ+3azSbsboQS+gu8eFDEqz/Jm//GbZuDtj4YeLw3w8y8IBFcG9f9dgobm1vbO8Xd0t7+weFR+fikreNUMWyxWMSqG1CNgktsGW4EdhOFNAoEdoLJ3dzvPKHSPJYPZpqgH9GR5CFn1FipyQblilt1FyDrxMtJBXI0BuWv/jBmaYTSMEG17nluYvyMKsOZwFmpn2pMKJvQEfYslTRC7WeLQ2fkwipDEsbKljRkof6eyGik9TQKbGdEzVivenPxP6+XmvDGz7hMUoOSLReFqSAmJvOvyZArZEZMLaFMcXsrYWOqKDM2m5INwVt9eZ20r6qeW/Wa15X6bR5HEc7gHC7BgxrU4R4a0AIGCM/wCm/Oo/PivDsfy9aCk8+cwh84nz/GZYzn</latexit><latexit sha1_base64="MK9TkR1XfIASB2dfp9XX0/+fbPE=">AAAB6HicbVBNS8NAEJ3Ur1q/qh69LBbBU0lEqMeiF48t2A9oQ9lsJ+3azSbsboQS+gu8eFDEqz/Jm//GbZuDtj4YeLw3w8y8IBFcG9f9dgobm1vbO8Xd0t7+weFR+fikreNUMWyxWMSqG1CNgktsGW4EdhOFNAoEdoLJ3dzvPKHSPJYPZpqgH9GR5CFn1FipyQblilt1FyDrxMtJBXI0BuWv/jBmaYTSMEG17nluYvyMKsOZwFmpn2pMKJvQEfYslTRC7WeLQ2fkwipDEsbKljRkof6eyGik9TQKbGdEzVivenPxP6+XmvDGz7hMUoOSLReFqSAmJvOvyZArZEZMLaFMcXsrYWOqKDM2m5INwVt9eZ20r6qeW/Wa15X6bR5HEc7gHC7BgxrU4R4a0AIGCM/wCm/Oo/PivDsfy9aCk8+cwh84nz/GZYzn</latexit><latexit sha1_base64="MK9TkR1XfIASB2dfp9XX0/+fbPE=">AAAB6HicbVBNS8NAEJ3Ur1q/qh69LBbBU0lEqMeiF48t2A9oQ9lsJ+3azSbsboQS+gu8eFDEqz/Jm//GbZuDtj4YeLw3w8y8IBFcG9f9dgobm1vbO8Xd0t7+weFR+fikreNUMWyxWMSqG1CNgktsGW4EdhOFNAoEdoLJ3dzvPKHSPJYPZpqgH9GR5CFn1FipyQblilt1FyDrxMtJBXI0BuWv/jBmaYTSMEG17nluYvyMKsOZwFmpn2pMKJvQEfYslTRC7WeLQ2fkwipDEsbKljRkof6eyGik9TQKbGdEzVivenPxP6+XmvDGz7hMUoOSLReFqSAmJvOvyZArZEZMLaFMcXsrYWOqKDM2m5INwVt9eZ20r6qeW/Wa15X6bR5HEc7gHC7BgxrU4R4a0AIGCM/wCm/Oo/PivDsfy9aCk8+cwh84nz/GZYzn</latexit><latexit sha1_base64="MK9TkR1XfIASB2dfp9XX0/+fbPE=">AAAB6HicbVBNS8NAEJ3Ur1q/qh69LBbBU0lEqMeiF48t2A9oQ9lsJ+3azSbsboQS+gu8eFDEqz/Jm//GbZuDtj4YeLw3w8y8IBFcG9f9dgobm1vbO8Xd0t7+weFR+fikreNUMWyxWMSqG1CNgktsGW4EdhOFNAoEdoLJ3dzvPKHSPJYPZpqgH9GR5CFn1FipyQblilt1FyDrxMtJBXI0BuWv/jBmaYTSMEG17nluYvyMKsOZwFmpn2pMKJvQEfYslTRC7WeLQ2fkwipDEsbKljRkof6eyGik9TQKbGdEzVivenPxP6+XmvDGz7hMUoOSLReFqSAmJvOvyZArZEZMLaFMcXsrYWOqKDM2m5INwVt9eZ20r6qeW/Wa15X6bR5HEc7gHC7BgxrU4R4a0AIGCM/wCm/Oo/PivDsfy9aCk8+cwh84nz/GZYzn</latexit>

size/order of loops, threading choices,

memory scope, …

Page 114: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Vanilla Cost ModelingHigh-level

Configurationsc<latexit sha1_base64="MK9TkR1XfIASB2dfp9XX0/+fbPE=">AAAB6HicbVBNS8NAEJ3Ur1q/qh69LBbBU0lEqMeiF48t2A9oQ9lsJ+3azSbsboQS+gu8eFDEqz/Jm//GbZuDtj4YeLw3w8y8IBFcG9f9dgobm1vbO8Xd0t7+weFR+fikreNUMWyxWMSqG1CNgktsGW4EdhOFNAoEdoLJ3dzvPKHSPJYPZpqgH9GR5CFn1FipyQblilt1FyDrxMtJBXI0BuWv/jBmaYTSMEG17nluYvyMKsOZwFmpn2pMKJvQEfYslTRC7WeLQ2fkwipDEsbKljRkof6eyGik9TQKbGdEzVivenPxP6+XmvDGz7hMUoOSLReFqSAmJvOvyZArZEZMLaFMcXsrYWOqKDM2m5INwVt9eZ20r6qeW/Wa15X6bR5HEc7gHC7BgxrU4R4a0AIGCM/wCm/Oo/PivDsfy9aCk8+cwh84nz/GZYzn</latexit><latexit sha1_base64="MK9TkR1XfIASB2dfp9XX0/+fbPE=">AAAB6HicbVBNS8NAEJ3Ur1q/qh69LBbBU0lEqMeiF48t2A9oQ9lsJ+3azSbsboQS+gu8eFDEqz/Jm//GbZuDtj4YeLw3w8y8IBFcG9f9dgobm1vbO8Xd0t7+weFR+fikreNUMWyxWMSqG1CNgktsGW4EdhOFNAoEdoLJ3dzvPKHSPJYPZpqgH9GR5CFn1FipyQblilt1FyDrxMtJBXI0BuWv/jBmaYTSMEG17nluYvyMKsOZwFmpn2pMKJvQEfYslTRC7WeLQ2fkwipDEsbKljRkof6eyGik9TQKbGdEzVivenPxP6+XmvDGz7hMUoOSLReFqSAmJvOvyZArZEZMLaFMcXsrYWOqKDM2m5INwVt9eZ20r6qeW/Wa15X6bR5HEc7gHC7BgxrU4R4a0AIGCM/wCm/Oo/PivDsfy9aCk8+cwh84nz/GZYzn</latexit><latexit sha1_base64="MK9TkR1XfIASB2dfp9XX0/+fbPE=">AAAB6HicbVBNS8NAEJ3Ur1q/qh69LBbBU0lEqMeiF48t2A9oQ9lsJ+3azSbsboQS+gu8eFDEqz/Jm//GbZuDtj4YeLw3w8y8IBFcG9f9dgobm1vbO8Xd0t7+weFR+fikreNUMWyxWMSqG1CNgktsGW4EdhOFNAoEdoLJ3dzvPKHSPJYPZpqgH9GR5CFn1FipyQblilt1FyDrxMtJBXI0BuWv/jBmaYTSMEG17nluYvyMKsOZwFmpn2pMKJvQEfYslTRC7WeLQ2fkwipDEsbKljRkof6eyGik9TQKbGdEzVivenPxP6+XmvDGz7hMUoOSLReFqSAmJvOvyZArZEZMLaFMcXsrYWOqKDM2m5INwVt9eZ20r6qeW/Wa15X6bR5HEc7gHC7BgxrU4R4a0AIGCM/wCm/Oo/PivDsfy9aCk8+cwh84nz/GZYzn</latexit><latexit sha1_base64="MK9TkR1XfIASB2dfp9XX0/+fbPE=">AAAB6HicbVBNS8NAEJ3Ur1q/qh69LBbBU0lEqMeiF48t2A9oQ9lsJ+3azSbsboQS+gu8eFDEqz/Jm//GbZuDtj4YeLw3w8y8IBFcG9f9dgobm1vbO8Xd0t7+weFR+fikreNUMWyxWMSqG1CNgktsGW4EdhOFNAoEdoLJ3dzvPKHSPJYPZpqgH9GR5CFn1FipyQblilt1FyDrxMtJBXI0BuWv/jBmaYTSMEG17nluYvyMKsOZwFmpn2pMKJvQEfYslTRC7WeLQ2fkwipDEsbKljRkof6eyGik9TQKbGdEzVivenPxP6+XmvDGz7hMUoOSLReFqSAmJvOvyZArZEZMLaFMcXsrYWOqKDM2m5INwVt9eZ20r6qeW/Wa15X6bR5HEc7gHC7BgxrU4R4a0AIGCM/wCm/Oo/PivDsfy9aCk8+cwh84nz/GZYzn</latexit>

size/order of loops, threading choices,

memory scope, …

Feature Vector

Flatten as feature vector

Page 115: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Vanilla Cost ModelingHigh-level

Configurations

Your Favorite Model

+ +

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size/order of loops, threading choices,

memory scope, …

Feature Vector

Flatten as feature vector

Page 116: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Vanilla Cost ModelingHigh-level

Configurations

Your Favorite Model

+ +

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size/order of loops, threading choices,

memory scope, …

Feature Vector

Flatten as feature vector

Drawback: Ignores domain knowledge Set of configurations can differ per task (task dependent)

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Program-aware Modeling: Tree-based Approach

High-level Configurationsc

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Program-aware Modeling: Tree-based Approach

High-level Configurations

for y in range(8): for x in range(8): C[y][x]=0 for k in range(8): C[y][x]+=A[k][y]*B[k][x]

Low-level Abstract Syntax Tree (AST)

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Page 119: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Program-aware Modeling: Tree-based Approach

High-level Configurations

for y in range(8): for x in range(8): C[y][x]=0 for k in range(8): C[y][x]+=A[k][y]*B[k][x]

Low-level Abstract Syntax Tree (AST)

C A By 64 64 64x 8 8 64k 1 8 8

y 1x 8k 64

touched memory

outer looplength

Statistical features of AST

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Page 120: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Program-aware Modeling: Tree-based Approach

High-level Configurations

for y in range(8): for x in range(8): C[y][x]=0 for k in range(8): C[y][x]+=A[k][y]*B[k][x]

Low-level Abstract Syntax Tree (AST)

C A By 64 64 64x 8 8 64k 1 8 8

y 1x 8k 64

touched memory

outer looplength

Statistical features of AST

Your Favorite Model

+ +

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Page 121: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Program-aware Modeling: Tree-based Approach

High-level Configurations

for y in range(8): for x in range(8): C[y][x]=0 for k in range(8): C[y][x]+=A[k][y]*B[k][x]

Low-level Abstract Syntax Tree (AST)

C A By 64 64 64x 8 8 64k 1 8 8

y 1x 8k 64

touched memory

outer looplength

Statistical features of AST

Your Favorite Model

+ +

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Benefit: low-level AST is a common representation (task invariant)

Page 122: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Program-aware Modeling: Neural Approach

High-level Configurations

for y in range(8): for x in range(8): C[y][x]=0 for k in range(8): C[y][x]+=A[k][y]*B[k][x]

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Low-level Abstract Syntax Tree (AST)

Page 123: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Program-aware Modeling: Neural Approach

High-level Configurations

for y in range(8): for x in range(8): C[y][x]=0 for k in range(8): C[y][x]+=A[k][y]*B[k][x]

for

context vec of x

for

for

context vec of y

context vec of k

+

soft scatter

finalembedding

TreeGRU on ASTc<latexit sha1_base64="MK9TkR1XfIASB2dfp9XX0/+fbPE=">AAAB6HicbVBNS8NAEJ3Ur1q/qh69LBbBU0lEqMeiF48t2A9oQ9lsJ+3azSbsboQS+gu8eFDEqz/Jm//GbZuDtj4YeLw3w8y8IBFcG9f9dgobm1vbO8Xd0t7+weFR+fikreNUMWyxWMSqG1CNgktsGW4EdhOFNAoEdoLJ3dzvPKHSPJYPZpqgH9GR5CFn1FipyQblilt1FyDrxMtJBXI0BuWv/jBmaYTSMEG17nluYvyMKsOZwFmpn2pMKJvQEfYslTRC7WeLQ2fkwipDEsbKljRkof6eyGik9TQKbGdEzVivenPxP6+XmvDGz7hMUoOSLReFqSAmJvOvyZArZEZMLaFMcXsrYWOqKDM2m5INwVt9eZ20r6qeW/Wa15X6bR5HEc7gHC7BgxrU4R4a0AIGCM/wCm/Oo/PivDsfy9aCk8+cwh84nz/GZYzn</latexit><latexit sha1_base64="MK9TkR1XfIASB2dfp9XX0/+fbPE=">AAAB6HicbVBNS8NAEJ3Ur1q/qh69LBbBU0lEqMeiF48t2A9oQ9lsJ+3azSbsboQS+gu8eFDEqz/Jm//GbZuDtj4YeLw3w8y8IBFcG9f9dgobm1vbO8Xd0t7+weFR+fikreNUMWyxWMSqG1CNgktsGW4EdhOFNAoEdoLJ3dzvPKHSPJYPZpqgH9GR5CFn1FipyQblilt1FyDrxMtJBXI0BuWv/jBmaYTSMEG17nluYvyMKsOZwFmpn2pMKJvQEfYslTRC7WeLQ2fkwipDEsbKljRkof6eyGik9TQKbGdEzVivenPxP6+XmvDGz7hMUoOSLReFqSAmJvOvyZArZEZMLaFMcXsrYWOqKDM2m5INwVt9eZ20r6qeW/Wa15X6bR5HEc7gHC7BgxrU4R4a0AIGCM/wCm/Oo/PivDsfy9aCk8+cwh84nz/GZYzn</latexit><latexit sha1_base64="MK9TkR1XfIASB2dfp9XX0/+fbPE=">AAAB6HicbVBNS8NAEJ3Ur1q/qh69LBbBU0lEqMeiF48t2A9oQ9lsJ+3azSbsboQS+gu8eFDEqz/Jm//GbZuDtj4YeLw3w8y8IBFcG9f9dgobm1vbO8Xd0t7+weFR+fikreNUMWyxWMSqG1CNgktsGW4EdhOFNAoEdoLJ3dzvPKHSPJYPZpqgH9GR5CFn1FipyQblilt1FyDrxMtJBXI0BuWv/jBmaYTSMEG17nluYvyMKsOZwFmpn2pMKJvQEfYslTRC7WeLQ2fkwipDEsbKljRkof6eyGik9TQKbGdEzVivenPxP6+XmvDGz7hMUoOSLReFqSAmJvOvyZArZEZMLaFMcXsrYWOqKDM2m5INwVt9eZ20r6qeW/Wa15X6bR5HEc7gHC7BgxrU4R4a0AIGCM/wCm/Oo/PivDsfy9aCk8+cwh84nz/GZYzn</latexit><latexit sha1_base64="MK9TkR1XfIASB2dfp9XX0/+fbPE=">AAAB6HicbVBNS8NAEJ3Ur1q/qh69LBbBU0lEqMeiF48t2A9oQ9lsJ+3azSbsboQS+gu8eFDEqz/Jm//GbZuDtj4YeLw3w8y8IBFcG9f9dgobm1vbO8Xd0t7+weFR+fikreNUMWyxWMSqG1CNgktsGW4EdhOFNAoEdoLJ3dzvPKHSPJYPZpqgH9GR5CFn1FipyQblilt1FyDrxMtJBXI0BuWv/jBmaYTSMEG17nluYvyMKsOZwFmpn2pMKJvQEfYslTRC7WeLQ2fkwipDEsbKljRkof6eyGik9TQKbGdEzVivenPxP6+XmvDGz7hMUoOSLReFqSAmJvOvyZArZEZMLaFMcXsrYWOqKDM2m5INwVt9eZ20r6qeW/Wa15X6bR5HEc7gHC7BgxrU4R4a0AIGCM/wCm/Oo/PivDsfy9aCk8+cwh84nz/GZYzn</latexit>

Low-level Abstract Syntax Tree (AST)

Page 124: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Comparisons of Models

Task Invariant Time Cost Predictive Accuracy

Vanilla Model No Low Medium

Tree-based Model Yes Low Good

Neural Model Yes High Good

Page 125: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Comparisons of Models

Task Invariant Time Cost Predictive Accuracy

Vanilla Model No Low Medium

Tree-based Model Yes Low Good

Neural Model Yes High Good

Choose tree-based model by default

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0 100 200 300 400 500 600 700 8000.00

0.25

0.50

0.75

1.00

1.25

1.50

Effectiveness of ML based Model

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0 100 200 300 400 500 600 700 8000.00

0.25

0.50

0.75

1.00

1.25

1.50

Effectiveness of ML based Model

One Conv2D Layer of ResNet18 on Titan X

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0 100 200 300 400 500 600 700 8000.00

0.25

0.50

0.75

1.00

1.25

1.50

Effectiveness of ML based Model

Number of Trials

One Conv2D Layer of ResNet18 on Titan X

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0 100 200 300 400 500 600 700 8000.00

0.25

0.50

0.75

1.00

1.25

1.50

Effectiveness of ML based Model

Number of Trials

One Conv2D Layer of ResNet18 on Titan X

Rela

tive

Spee

dup

Baseline: CuDNN

Page 130: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

0 100 200 300 400 500 600 700 8000.00

0.25

0.50

0.75

1.00

1.25

1.50

Effectiveness of ML based Model

Number of Trials

One Conv2D Layer of ResNet18 on Titan X

AutoTVM: Black-box OptimizationRe

lativ

e Sp

eedu

p

Baseline: CuDNN

Page 131: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

0 100 200 300 400 500 600 700 8000.00

0.25

0.50

0.75

1.00

1.25

1.50

Effectiveness of ML based Model

One Conv2D Layer of ResNet18 on Titan X

AutoTVM: ML-based Model

Number of Trials

AutoTVM: Black-box OptimizationRe

lativ

e Sp

eedu

p

Baseline: CuDNN

Page 132: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Comparisons of Models

Task Invariant Time Cost Predictive Accuracy

Vanilla Model No Low Medium

Tree-based Model Yes Low Good

Neural Model Yes High Good

Page 133: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Comparisons of Models

Task Invariant Time Cost Predictive Accuracy

Vanilla Model No Low Medium

Tree-based Model Yes Low Good

Neural Model Yes High Good

Page 134: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Unique Problem Characteristics

Large number of similar tasks

Relatively low experiment cost

Program-aware modeling

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Search Space

Expression AutoTVM Code Generatorc

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x

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Training data

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Statistical Cost Model

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learningf̂(e, c)

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Page 135: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Transferable Cost Model

Search Space

Expression AutoTVM Code Generatorc

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x

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Historical data from related operators (tasks)

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Page 137: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

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Expression AutoTVM Code Generatorc

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Historical data from related operators (tasks)

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Expression

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e2<latexit sha1_base64="mfO9ARkQyRLj4TbGagbpzJgRzY0=">AAAB6nicbVBNS8NAEJ3Ur1q/qh69LBbBU0mKUI9FLx4r2g9oQ9lsJ+3SzSbsboQS+hO8eFDEq7/Im//GbZuDtj4YeLw3w8y8IBFcG9f9dgobm1vbO8Xd0t7+weFR+fikreNUMWyxWMSqG1CNgktsGW4EdhOFNAoEdoLJ7dzvPKHSPJaPZpqgH9GR5CFn1FjpAQe1QbniVt0FyDrxclKBHM1B+as/jFkaoTRMUK17npsYP6PKcCZwVuqnGhPKJnSEPUsljVD72eLUGbmwypCEsbIlDVmovycyGmk9jQLbGVEz1qveXPzP66UmvPYzLpPUoGTLRWEqiInJ/G8y5AqZEVNLKFPc3krYmCrKjE2nZEPwVl9eJ+1a1XOr3v1VpXGTx1GEMziHS/CgDg24gya0gMEInuEV3hzhvDjvzseyteDkM6fwB87nD/AzjY4=</latexit><latexit sha1_base64="mfO9ARkQyRLj4TbGagbpzJgRzY0=">AAAB6nicbVBNS8NAEJ3Ur1q/qh69LBbBU0mKUI9FLx4r2g9oQ9lsJ+3SzSbsboQS+hO8eFDEq7/Im//GbZuDtj4YeLw3w8y8IBFcG9f9dgobm1vbO8Xd0t7+weFR+fikreNUMWyxWMSqG1CNgktsGW4EdhOFNAoEdoLJ7dzvPKHSPJaPZpqgH9GR5CFn1FjpAQe1QbniVt0FyDrxclKBHM1B+as/jFkaoTRMUK17npsYP6PKcCZwVuqnGhPKJnSEPUsljVD72eLUGbmwypCEsbIlDVmovycyGmk9jQLbGVEz1qveXPzP66UmvPYzLpPUoGTLRWEqiInJ/G8y5AqZEVNLKFPc3krYmCrKjE2nZEPwVl9eJ+1a1XOr3v1VpXGTx1GEMziHS/CgDg24gya0gMEInuEV3hzhvDjvzseyteDkM6fwB87nD/AzjY4=</latexit><latexit sha1_base64="mfO9ARkQyRLj4TbGagbpzJgRzY0=">AAAB6nicbVBNS8NAEJ3Ur1q/qh69LBbBU0mKUI9FLx4r2g9oQ9lsJ+3SzSbsboQS+hO8eFDEq7/Im//GbZuDtj4YeLw3w8y8IBFcG9f9dgobm1vbO8Xd0t7+weFR+fikreNUMWyxWMSqG1CNgktsGW4EdhOFNAoEdoLJ7dzvPKHSPJaPZpqgH9GR5CFn1FjpAQe1QbniVt0FyDrxclKBHM1B+as/jFkaoTRMUK17npsYP6PKcCZwVuqnGhPKJnSEPUsljVD72eLUGbmwypCEsbIlDVmovycyGmk9jQLbGVEz1qveXPzP66UmvPYzLpPUoGTLRWEqiInJ/G8y5AqZEVNLKFPc3krYmCrKjE2nZEPwVl9eJ+1a1XOr3v1VpXGTx1GEMziHS/CgDg24gya0gMEInuEV3hzhvDjvzseyteDkM6fwB87nD/AzjY4=</latexit><latexit sha1_base64="mfO9ARkQyRLj4TbGagbpzJgRzY0=">AAAB6nicbVBNS8NAEJ3Ur1q/qh69LBbBU0mKUI9FLx4r2g9oQ9lsJ+3SzSbsboQS+hO8eFDEq7/Im//GbZuDtj4YeLw3w8y8IBFcG9f9dgobm1vbO8Xd0t7+weFR+fikreNUMWyxWMSqG1CNgktsGW4EdhOFNAoEdoLJ7dzvPKHSPJaPZpqgH9GR5CFn1FjpAQe1QbniVt0FyDrxclKBHM1B+as/jFkaoTRMUK17npsYP6PKcCZwVuqnGhPKJnSEPUsljVD72eLUGbmwypCEsbIlDVmovycyGmk9jQLbGVEz1qveXPzP66UmvPYzLpPUoGTLRWEqiInJ/G8y5AqZEVNLKFPc3krYmCrKjE2nZEPwVl9eJ+1a1XOr3v1VpXGTx1GEMziHS/CgDg24gya0gMEInuEV3hzhvDjvzseyteDkM6fwB87nD/AzjY4=</latexit>

New Tasks

Page 138: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Transferable Cost Model

Search Space

Expression AutoTVM Code Generatorc

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x

<latexit sha1_base64="aU2pysM3BHfN5UL25fiuMETI3Uw=">AAAB6XicbVBNS8NAEJ3Ur1q/qh69LBbBU0lE0GPRi8cqthbaUDbbTbt0swm7E7GE/gMvHhTx6j/y5r9x0+agrQ8GHu/NMDMvSKQw6LrfTmlldW19o7xZ2dre2d2r7h+0TZxqxlsslrHuBNRwKRRvoUDJO4nmNAokfwjG17n/8Mi1EbG6x0nC/YgOlQgFo2ilu6dKv1pz6+4MZJl4BalBgWa/+tUbxCyNuEImqTFdz03Qz6hGwSSfVnqp4QllYzrkXUsVjbjxs9mlU3JilQEJY21LIZmpvycyGhkziQLbGVEcmUUvF//zuimGl34mVJIiV2y+KEwlwZjkb5OB0JyhnFhCmRb2VsJGVFOGNpw8BG/x5WXSPqt7bt27Pa81roo4ynAEx3AKHlxAA26gCS1gEMIzvMKbM3ZenHfnY95acoqZQ/gD5/MHGsyNEA==</latexit><latexit sha1_base64="aU2pysM3BHfN5UL25fiuMETI3Uw=">AAAB6XicbVBNS8NAEJ3Ur1q/qh69LBbBU0lE0GPRi8cqthbaUDbbTbt0swm7E7GE/gMvHhTx6j/y5r9x0+agrQ8GHu/NMDMvSKQw6LrfTmlldW19o7xZ2dre2d2r7h+0TZxqxlsslrHuBNRwKRRvoUDJO4nmNAokfwjG17n/8Mi1EbG6x0nC/YgOlQgFo2ilu6dKv1pz6+4MZJl4BalBgWa/+tUbxCyNuEImqTFdz03Qz6hGwSSfVnqp4QllYzrkXUsVjbjxs9mlU3JilQEJY21LIZmpvycyGhkziQLbGVEcmUUvF//zuimGl34mVJIiV2y+KEwlwZjkb5OB0JyhnFhCmRb2VsJGVFOGNpw8BG/x5WXSPqt7bt27Pa81roo4ynAEx3AKHlxAA26gCS1gEMIzvMKbM3ZenHfnY95acoqZQ/gD5/MHGsyNEA==</latexit><latexit sha1_base64="aU2pysM3BHfN5UL25fiuMETI3Uw=">AAAB6XicbVBNS8NAEJ3Ur1q/qh69LBbBU0lE0GPRi8cqthbaUDbbTbt0swm7E7GE/gMvHhTx6j/y5r9x0+agrQ8GHu/NMDMvSKQw6LrfTmlldW19o7xZ2dre2d2r7h+0TZxqxlsslrHuBNRwKRRvoUDJO4nmNAokfwjG17n/8Mi1EbG6x0nC/YgOlQgFo2ilu6dKv1pz6+4MZJl4BalBgWa/+tUbxCyNuEImqTFdz03Qz6hGwSSfVnqp4QllYzrkXUsVjbjxs9mlU3JilQEJY21LIZmpvycyGhkziQLbGVEcmUUvF//zuimGl34mVJIiV2y+KEwlwZjkb5OB0JyhnFhCmRb2VsJGVFOGNpw8BG/x5WXSPqt7bt27Pa81roo4ynAEx3AKHlxAA26gCS1gEMIzvMKbM3ZenHfnY95acoqZQ/gD5/MHGsyNEA==</latexit><latexit sha1_base64="aU2pysM3BHfN5UL25fiuMETI3Uw=">AAAB6XicbVBNS8NAEJ3Ur1q/qh69LBbBU0lE0GPRi8cqthbaUDbbTbt0swm7E7GE/gMvHhTx6j/y5r9x0+agrQ8GHu/NMDMvSKQw6LrfTmlldW19o7xZ2dre2d2r7h+0TZxqxlsslrHuBNRwKRRvoUDJO4nmNAokfwjG17n/8Mi1EbG6x0nC/YgOlQgFo2ilu6dKv1pz6+4MZJl4BalBgWa/+tUbxCyNuEImqTFdz03Qz6hGwSSfVnqp4QllYzrkXUsVjbjxs9mlU3JilQEJY21LIZmpvycyGhkziQLbGVEcmUUvF//zuimGl34mVJIiV2y+KEwlwZjkb5OB0JyhnFhCmRb2VsJGVFOGNpw8BG/x5WXSPqt7bt27Pa81roo4ynAEx3AKHlxAA26gCS1gEMIzvMKbM3ZenHfnY95acoqZQ/gD5/MHGsyNEA==</latexit>

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Historical data from related operators (tasks)

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Se1<latexit sha1_base64="S3wLawgxEK+jZJ/7efPwhwug0jQ=">AAAB73icbVBNS8NAEJ3Ur1q/qh69LBbBU0lE0GPRi8eKthbaEDbbabt0s4m7G6GE/gkvHhTx6t/x5r9x0+agrQ8GHu/NMDMvTATXxnW/ndLK6tr6RnmzsrW9s7tX3T9o6zhVDFssFrHqhFSj4BJbhhuBnUQhjUKBD+H4OvcfnlBpHst7M0nQj+hQ8gFn1FipcxdkGHjTSlCtuXV3BrJMvILUoEAzqH71+jFLI5SGCap113MT42dUGc4ETiu9VGNC2ZgOsWuppBFqP5vdOyUnVumTQaxsSUNm6u+JjEZaT6LQdkbUjPSil4v/ed3UDC79jMskNSjZfNEgFcTEJH+e9LlCZsTEEsoUt7cSNqKKMmMjykPwFl9eJu2zuufWvdvzWuOqiKMMR3AMp+DBBTTgBprQAgYCnuEV3pxH58V5dz7mrSWnmDmEP3A+fwBFpI9z</latexit><latexit sha1_base64="S3wLawgxEK+jZJ/7efPwhwug0jQ=">AAAB73icbVBNS8NAEJ3Ur1q/qh69LBbBU0lE0GPRi8eKthbaEDbbabt0s4m7G6GE/gkvHhTx6t/x5r9x0+agrQ8GHu/NMDMvTATXxnW/ndLK6tr6RnmzsrW9s7tX3T9o6zhVDFssFrHqhFSj4BJbhhuBnUQhjUKBD+H4OvcfnlBpHst7M0nQj+hQ8gFn1FipcxdkGHjTSlCtuXV3BrJMvILUoEAzqH71+jFLI5SGCap113MT42dUGc4ETiu9VGNC2ZgOsWuppBFqP5vdOyUnVumTQaxsSUNm6u+JjEZaT6LQdkbUjPSil4v/ed3UDC79jMskNSjZfNEgFcTEJH+e9LlCZsTEEsoUt7cSNqKKMmMjykPwFl9eJu2zuufWvdvzWuOqiKMMR3AMp+DBBTTgBprQAgYCnuEV3pxH58V5dz7mrSWnmDmEP3A+fwBFpI9z</latexit><latexit sha1_base64="S3wLawgxEK+jZJ/7efPwhwug0jQ=">AAAB73icbVBNS8NAEJ3Ur1q/qh69LBbBU0lE0GPRi8eKthbaEDbbabt0s4m7G6GE/gkvHhTx6t/x5r9x0+agrQ8GHu/NMDMvTATXxnW/ndLK6tr6RnmzsrW9s7tX3T9o6zhVDFssFrHqhFSj4BJbhhuBnUQhjUKBD+H4OvcfnlBpHst7M0nQj+hQ8gFn1FipcxdkGHjTSlCtuXV3BrJMvILUoEAzqH71+jFLI5SGCap113MT42dUGc4ETiu9VGNC2ZgOsWuppBFqP5vdOyUnVumTQaxsSUNm6u+JjEZaT6LQdkbUjPSil4v/ed3UDC79jMskNSjZfNEgFcTEJH+e9LlCZsTEEsoUt7cSNqKKMmMjykPwFl9eJu2zuufWvdvzWuOqiKMMR3AMp+DBBTTgBprQAgYCnuEV3pxH58V5dz7mrSWnmDmEP3A+fwBFpI9z</latexit><latexit sha1_base64="S3wLawgxEK+jZJ/7efPwhwug0jQ=">AAAB73icbVBNS8NAEJ3Ur1q/qh69LBbBU0lE0GPRi8eKthbaEDbbabt0s4m7G6GE/gkvHhTx6t/x5r9x0+agrQ8GHu/NMDMvTATXxnW/ndLK6tr6RnmzsrW9s7tX3T9o6zhVDFssFrHqhFSj4BJbhhuBnUQhjUKBD+H4OvcfnlBpHst7M0nQj+hQ8gFn1FipcxdkGHjTSlCtuXV3BrJMvILUoEAzqH71+jFLI5SGCap113MT42dUGc4ETiu9VGNC2ZgOsWuppBFqP5vdOyUnVumTQaxsSUNm6u+JjEZaT6LQdkbUjPSil4v/ed3UDC79jMskNSjZfNEgFcTEJH+e9LlCZsTEEsoUt7cSNqKKMmMjykPwFl9eJu2zuufWvdvzWuOqiKMMR3AMp+DBBTTgBprQAgYCnuEV3pxH58V5dz7mrSWnmDmEP3A+fwBFpI9z</latexit>

Shared Cost Model

Search Space

Expression

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New Tasks

Page 139: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Transferable Cost Model

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Expression AutoTVM Code Generatorc

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Historical data from related operators (tasks)

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Shared Cost Model

Code Generatorc

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Search Space

Expression

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New Tasks

Page 140: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Transferable Cost Model

Search Space

Expression AutoTVM Code Generatorc

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x

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Historical data from related operators (tasks)

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Shared Cost Model

Code Generatorc

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Search Space

Expression

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New TasksNeed task invariant

representation

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0 100 200 300 400 500 600 700 8000.00

0.25

0.50

0.75

1.00

1.25

1.50

Impact of Transfer Learning

AutoTVM: ML-based Model

Number of Trials

AutoTVM: Black-box OptimizationRe

lativ

e Sp

eedu

p

Baseline: CuDNN

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0 100 200 300 400 500 600 700 8000.00

0.25

0.50

0.75

1.00

1.25

1.50

Impact of Transfer Learning

Number of Trials

AutoTVM: Black-box OptimizationRe

lativ

e Sp

eedu

p

Baseline: CuDNN

Transferred ML-Model

AutoTVM: ML-based Model

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0 100 200 300 400 500 600 700 8000.00

0.25

0.50

0.75

1.00

1.25

1.50

Impact of Transfer Learning

Number of Trials

AutoTVM: Black-box OptimizationRe

lativ

e Sp

eedu

p

Baseline: CuDNN

Transferred ML-Model

AutoTVM: ML-based Model

3x to 10x speedup over non-transfer case

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Learning to Optimize Tensor Programs

Learning to Optimize Tensor Programs. Chen et al. NeurIPS 18

Large number of similar tasks

Relatively low experiment cost

Program-aware modeling

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Search Space

Expression AutoTVM Code Generatorc

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x

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D<latexit sha1_base64="1Z6CzjBl0OMVztfQ+m452YDkcY0=">AAAB8nicbVDLSsNAFL2pr1pfVZdugkVwVRIRdFnUhcsK9gFtKJPppB06mQkzN0IJ/Qw3LhRx69e482+ctFlo64GBwzn3MueeMBHcoOd9O6W19Y3NrfJ2ZWd3b/+genjUNirVlLWoEkp3Q2KY4JK1kKNg3UQzEoeCdcLJbe53npg2XMlHnCYsiMlI8ohTglbq9WOCY0pEdjcbVGte3ZvDXSV+QWpQoDmofvWHiqYxk0gFMabnewkGGdHIqWCzSj81LCF0QkasZ6kkMTNBNo88c8+sMnQjpe2T6M7V3xsZiY2ZxqGdzCOaZS8X//N6KUbXQcZlkiKTdPFRlAoXlZvf7w65ZhTF1BJCNbdZXTommlC0LVVsCf7yyaukfVH3vbr/cFlr3BR1lOEETuEcfLiCBtxDE1pAQcEzvMKbg86L8+58LEZLTrFzDH/gfP4AdN2RWg==</latexit><latexit sha1_base64="1Z6CzjBl0OMVztfQ+m452YDkcY0=">AAAB8nicbVDLSsNAFL2pr1pfVZdugkVwVRIRdFnUhcsK9gFtKJPppB06mQkzN0IJ/Qw3LhRx69e482+ctFlo64GBwzn3MueeMBHcoOd9O6W19Y3NrfJ2ZWd3b/+genjUNirVlLWoEkp3Q2KY4JK1kKNg3UQzEoeCdcLJbe53npg2XMlHnCYsiMlI8ohTglbq9WOCY0pEdjcbVGte3ZvDXSV+QWpQoDmofvWHiqYxk0gFMabnewkGGdHIqWCzSj81LCF0QkasZ6kkMTNBNo88c8+sMnQjpe2T6M7V3xsZiY2ZxqGdzCOaZS8X//N6KUbXQcZlkiKTdPFRlAoXlZvf7w65ZhTF1BJCNbdZXTommlC0LVVsCf7yyaukfVH3vbr/cFlr3BR1lOEETuEcfLiCBtxDE1pAQcEzvMKbg86L8+58LEZLTrFzDH/gfP4AdN2RWg==</latexit><latexit sha1_base64="1Z6CzjBl0OMVztfQ+m452YDkcY0=">AAAB8nicbVDLSsNAFL2pr1pfVZdugkVwVRIRdFnUhcsK9gFtKJPppB06mQkzN0IJ/Qw3LhRx69e482+ctFlo64GBwzn3MueeMBHcoOd9O6W19Y3NrfJ2ZWd3b/+genjUNirVlLWoEkp3Q2KY4JK1kKNg3UQzEoeCdcLJbe53npg2XMlHnCYsiMlI8ohTglbq9WOCY0pEdjcbVGte3ZvDXSV+QWpQoDmofvWHiqYxk0gFMabnewkGGdHIqWCzSj81LCF0QkasZ6kkMTNBNo88c8+sMnQjpe2T6M7V3xsZiY2ZxqGdzCOaZS8X//N6KUbXQcZlkiKTdPFRlAoXlZvf7w65ZhTF1BJCNbdZXTommlC0LVVsCf7yyaukfVH3vbr/cFlr3BR1lOEETuEcfLiCBtxDE1pAQcEzvMKbg86L8+58LEZLTrFzDH/gfP4AdN2RWg==</latexit><latexit sha1_base64="1Z6CzjBl0OMVztfQ+m452YDkcY0=">AAAB8nicbVDLSsNAFL2pr1pfVZdugkVwVRIRdFnUhcsK9gFtKJPppB06mQkzN0IJ/Qw3LhRx69e482+ctFlo64GBwzn3MueeMBHcoOd9O6W19Y3NrfJ2ZWd3b/+genjUNirVlLWoEkp3Q2KY4JK1kKNg3UQzEoeCdcLJbe53npg2XMlHnCYsiMlI8ohTglbq9WOCY0pEdjcbVGte3ZvDXSV+QWpQoDmofvWHiqYxk0gFMabnewkGGdHIqWCzSj81LCF0QkasZ6kkMTNBNo88c8+sMnQjpe2T6M7V3xsZiY2ZxqGdzCOaZS8X//N6KUbXQcZlkiKTdPFRlAoXlZvf7w65ZhTF1BJCNbdZXTommlC0LVVsCf7yyaukfVH3vbr/cFlr3BR1lOEETuEcfLiCBtxDE1pAQcEzvMKbg86L8+58LEZLTrFzDH/gfP4AdN2RWg==</latexit>

Training data

f(x)<latexit sha1_base64="Appt6dOASLoU0puF9XJna1LvMt4=">AAAB63icbVBNS8NAEJ3Ur1q/qh69LBahXkoigh6LXjxWMG2hDWWz3bRLdzdhdyOW0L/gxYMiXv1D3vw3btoctPXBwOO9GWbmhQln2rjut1NaW9/Y3CpvV3Z29/YPqodHbR2nilCfxDxW3RBrypmkvmGG026iKBYhp51wcpv7nUeqNIvlg5kmNBB4JFnECDa5FNWfzgfVmttw50CrxCtIDQq0BtWv/jAmqaDSEI617nluYoIMK8MIp7NKP9U0wWSCR7RnqcSC6iCb3zpDZ1YZoihWtqRBc/X3RIaF1lMR2k6BzVgve7n4n9dLTXQdZEwmqaGSLBZFKUcmRvnjaMgUJYZPLcFEMXsrImOsMDE2nooNwVt+eZW0Lxqe2/DuL2vNmyKOMpzAKdTBgytowh20wAcCY3iGV3hzhPPivDsfi9aSU8wcwx84nz9sX43R</latexit><latexit sha1_base64="Appt6dOASLoU0puF9XJna1LvMt4=">AAAB63icbVBNS8NAEJ3Ur1q/qh69LBahXkoigh6LXjxWMG2hDWWz3bRLdzdhdyOW0L/gxYMiXv1D3vw3btoctPXBwOO9GWbmhQln2rjut1NaW9/Y3CpvV3Z29/YPqodHbR2nilCfxDxW3RBrypmkvmGG026iKBYhp51wcpv7nUeqNIvlg5kmNBB4JFnECDa5FNWfzgfVmttw50CrxCtIDQq0BtWv/jAmqaDSEI617nluYoIMK8MIp7NKP9U0wWSCR7RnqcSC6iCb3zpDZ1YZoihWtqRBc/X3RIaF1lMR2k6BzVgve7n4n9dLTXQdZEwmqaGSLBZFKUcmRvnjaMgUJYZPLcFEMXsrImOsMDE2nooNwVt+eZW0Lxqe2/DuL2vNmyKOMpzAKdTBgytowh20wAcCY3iGV3hzhPPivDsfi9aSU8wcwx84nz9sX43R</latexit><latexit sha1_base64="Appt6dOASLoU0puF9XJna1LvMt4=">AAAB63icbVBNS8NAEJ3Ur1q/qh69LBahXkoigh6LXjxWMG2hDWWz3bRLdzdhdyOW0L/gxYMiXv1D3vw3btoctPXBwOO9GWbmhQln2rjut1NaW9/Y3CpvV3Z29/YPqodHbR2nilCfxDxW3RBrypmkvmGG026iKBYhp51wcpv7nUeqNIvlg5kmNBB4JFnECDa5FNWfzgfVmttw50CrxCtIDQq0BtWv/jAmqaDSEI617nluYoIMK8MIp7NKP9U0wWSCR7RnqcSC6iCb3zpDZ1YZoihWtqRBc/X3RIaF1lMR2k6BzVgve7n4n9dLTXQdZEwmqaGSLBZFKUcmRvnjaMgUJYZPLcFEMXsrImOsMDE2nooNwVt+eZW0Lxqe2/DuL2vNmyKOMpzAKdTBgytowh20wAcCY3iGV3hzhPPivDsfi9aSU8wcwx84nz9sX43R</latexit><latexit sha1_base64="Appt6dOASLoU0puF9XJna1LvMt4=">AAAB63icbVBNS8NAEJ3Ur1q/qh69LBahXkoigh6LXjxWMG2hDWWz3bRLdzdhdyOW0L/gxYMiXv1D3vw3btoctPXBwOO9GWbmhQln2rjut1NaW9/Y3CpvV3Z29/YPqodHbR2nilCfxDxW3RBrypmkvmGG026iKBYhp51wcpv7nUeqNIvlg5kmNBB4JFnECDa5FNWfzgfVmttw50CrxCtIDQq0BtWv/jAmqaDSEI617nluYoIMK8MIp7NKP9U0wWSCR7RnqcSC6iCb3zpDZ1YZoihWtqRBc/X3RIaF1lMR2k6BzVgve7n4n9dLTXQdZEwmqaGSLBZFKUcmRvnjaMgUJYZPLcFEMXsrImOsMDE2nooNwVt+eZW0Lxqe2/DuL2vNmyKOMpzAKdTBgytowh20wAcCY3iGV3hzhPPivDsfi9aSU8wcwx84nz9sX43R</latexit>

Statistical Cost Model

c<latexit sha1_base64="MK9TkR1XfIASB2dfp9XX0/+fbPE=">AAAB6HicbVBNS8NAEJ3Ur1q/qh69LBbBU0lEqMeiF48t2A9oQ9lsJ+3azSbsboQS+gu8eFDEqz/Jm//GbZuDtj4YeLw3w8y8IBFcG9f9dgobm1vbO8Xd0t7+weFR+fikreNUMWyxWMSqG1CNgktsGW4EdhOFNAoEdoLJ3dzvPKHSPJYPZpqgH9GR5CFn1FipyQblilt1FyDrxMtJBXI0BuWv/jBmaYTSMEG17nluYvyMKsOZwFmpn2pMKJvQEfYslTRC7WeLQ2fkwipDEsbKljRkof6eyGik9TQKbGdEzVivenPxP6+XmvDGz7hMUoOSLReFqSAmJvOvyZArZEZMLaFMcXsrYWOqKDM2m5INwVt9eZ20r6qeW/Wa15X6bR5HEc7gHC7BgxrU4R4a0AIGCM/wCm/Oo/PivDsfy9aCk8+cwh84nz/GZYzn</latexit><latexit sha1_base64="MK9TkR1XfIASB2dfp9XX0/+fbPE=">AAAB6HicbVBNS8NAEJ3Ur1q/qh69LBbBU0lEqMeiF48t2A9oQ9lsJ+3azSbsboQS+gu8eFDEqz/Jm//GbZuDtj4YeLw3w8y8IBFcG9f9dgobm1vbO8Xd0t7+weFR+fikreNUMWyxWMSqG1CNgktsGW4EdhOFNAoEdoLJ3dzvPKHSPJYPZpqgH9GR5CFn1FipyQblilt1FyDrxMtJBXI0BuWv/jBmaYTSMEG17nluYvyMKsOZwFmpn2pMKJvQEfYslTRC7WeLQ2fkwipDEsbKljRkof6eyGik9TQKbGdEzVivenPxP6+XmvDGz7hMUoOSLReFqSAmJvOvyZArZEZMLaFMcXsrYWOqKDM2m5INwVt9eZ20r6qeW/Wa15X6bR5HEc7gHC7BgxrU4R4a0AIGCM/wCm/Oo/PivDsfy9aCk8+cwh84nz/GZYzn</latexit><latexit sha1_base64="MK9TkR1XfIASB2dfp9XX0/+fbPE=">AAAB6HicbVBNS8NAEJ3Ur1q/qh69LBbBU0lEqMeiF48t2A9oQ9lsJ+3azSbsboQS+gu8eFDEqz/Jm//GbZuDtj4YeLw3w8y8IBFcG9f9dgobm1vbO8Xd0t7+weFR+fikreNUMWyxWMSqG1CNgktsGW4EdhOFNAoEdoLJ3dzvPKHSPJYPZpqgH9GR5CFn1FipyQblilt1FyDrxMtJBXI0BuWv/jBmaYTSMEG17nluYvyMKsOZwFmpn2pMKJvQEfYslTRC7WeLQ2fkwipDEsbKljRkof6eyGik9TQKbGdEzVivenPxP6+XmvDGz7hMUoOSLReFqSAmJvOvyZArZEZMLaFMcXsrYWOqKDM2m5INwVt9eZ20r6qeW/Wa15X6bR5HEc7gHC7BgxrU4R4a0AIGCM/wCm/Oo/PivDsfy9aCk8+cwh84nz/GZYzn</latexit><latexit sha1_base64="MK9TkR1XfIASB2dfp9XX0/+fbPE=">AAAB6HicbVBNS8NAEJ3Ur1q/qh69LBbBU0lEqMeiF48t2A9oQ9lsJ+3azSbsboQS+gu8eFDEqz/Jm//GbZuDtj4YeLw3w8y8IBFcG9f9dgobm1vbO8Xd0t7+weFR+fikreNUMWyxWMSqG1CNgktsGW4EdhOFNAoEdoLJ3dzvPKHSPJYPZpqgH9GR5CFn1FipyQblilt1FyDrxMtJBXI0BuWv/jBmaYTSMEG17nluYvyMKsOZwFmpn2pMKJvQEfYslTRC7WeLQ2fkwipDEsbKljRkof6eyGik9TQKbGdEzVivenPxP6+XmvDGz7hMUoOSLReFqSAmJvOvyZArZEZMLaFMcXsrYWOqKDM2m5INwVt9eZ20r6qeW/Wa15X6bR5HEc7gHC7BgxrU4R4a0AIGCM/wCm/Oo/PivDsfy9aCk8+cwh84nz/GZYzn</latexit>

learningf̂(e, c)

<latexit sha1_base64="16qeGbmTCS0jj1DslSxMOfKFjtQ=">AAAB9HicbVDLSgNBEOyNrxhfUY9eBoMQQcKuCHoMevEYwTwgWcLsZDYZMju7zvQGwpLv8OJBEa9+jDf/xsnjoIkFDUVVN91dQSKFQdf9dnJr6xubW/ntws7u3v5B8fCoYeJUM15nsYx1K6CGS6F4HQVK3ko0p1EgeTMY3k395ohrI2L1iOOE+xHtKxEKRtFKfmdAMQsnZX5B2Hm3WHIr7gxklXgLUoIFat3iV6cXszTiCpmkxrQ9N0E/oxoFk3xS6KSGJ5QNaZ+3LVU04sbPZkdPyJlVeiSMtS2FZKb+nshoZMw4CmxnRHFglr2p+J/XTjG88TOhkhS5YvNFYSoJxmSaAOkJzRnKsSWUaWFvJWxANWVocyrYELzll1dJ47LiuRXv4apUvV3EkYcTOIUyeHANVbiHGtSBwRM8wyu8OSPnxXl3PuatOWcxcwx/4Hz+AKUukVg=</latexit><latexit sha1_base64="16qeGbmTCS0jj1DslSxMOfKFjtQ=">AAAB9HicbVDLSgNBEOyNrxhfUY9eBoMQQcKuCHoMevEYwTwgWcLsZDYZMju7zvQGwpLv8OJBEa9+jDf/xsnjoIkFDUVVN91dQSKFQdf9dnJr6xubW/ntws7u3v5B8fCoYeJUM15nsYx1K6CGS6F4HQVK3ko0p1EgeTMY3k395ohrI2L1iOOE+xHtKxEKRtFKfmdAMQsnZX5B2Hm3WHIr7gxklXgLUoIFat3iV6cXszTiCpmkxrQ9N0E/oxoFk3xS6KSGJ5QNaZ+3LVU04sbPZkdPyJlVeiSMtS2FZKb+nshoZMw4CmxnRHFglr2p+J/XTjG88TOhkhS5YvNFYSoJxmSaAOkJzRnKsSWUaWFvJWxANWVocyrYELzll1dJ47LiuRXv4apUvV3EkYcTOIUyeHANVbiHGtSBwRM8wyu8OSPnxXl3PuatOWcxcwx/4Hz+AKUukVg=</latexit><latexit sha1_base64="16qeGbmTCS0jj1DslSxMOfKFjtQ=">AAAB9HicbVDLSgNBEOyNrxhfUY9eBoMQQcKuCHoMevEYwTwgWcLsZDYZMju7zvQGwpLv8OJBEa9+jDf/xsnjoIkFDUVVN91dQSKFQdf9dnJr6xubW/ntws7u3v5B8fCoYeJUM15nsYx1K6CGS6F4HQVK3ko0p1EgeTMY3k395ohrI2L1iOOE+xHtKxEKRtFKfmdAMQsnZX5B2Hm3WHIr7gxklXgLUoIFat3iV6cXszTiCpmkxrQ9N0E/oxoFk3xS6KSGJ5QNaZ+3LVU04sbPZkdPyJlVeiSMtS2FZKb+nshoZMw4CmxnRHFglr2p+J/XTjG88TOhkhS5YvNFYSoJxmSaAOkJzRnKsSWUaWFvJWxANWVocyrYELzll1dJ47LiuRXv4apUvV3EkYcTOIUyeHANVbiHGtSBwRM8wyu8OSPnxXl3PuatOWcxcwx/4Hz+AKUukVg=</latexit><latexit sha1_base64="16qeGbmTCS0jj1DslSxMOfKFjtQ=">AAAB9HicbVDLSgNBEOyNrxhfUY9eBoMQQcKuCHoMevEYwTwgWcLsZDYZMju7zvQGwpLv8OJBEa9+jDf/xsnjoIkFDUVVN91dQSKFQdf9dnJr6xubW/ntws7u3v5B8fCoYeJUM15nsYx1K6CGS6F4HQVK3ko0p1EgeTMY3k395ohrI2L1iOOE+xHtKxEKRtFKfmdAMQsnZX5B2Hm3WHIr7gxklXgLUoIFat3iV6cXszTiCpmkxrQ9N0E/oxoFk3xS6KSGJ5QNaZ+3LVU04sbPZkdPyJlVeiSMtS2FZKb+nshoZMw4CmxnRHFglr2p+J/XTjG88TOhkhS5YvNFYSoJxmSaAOkJzRnKsSWUaWFvJWxANWVocyrYELzll1dJ47LiuRXv4apUvV3EkYcTOIUyeHANVbiHGtSBwRM8wyu8OSPnxXl3PuatOWcxcwx/4Hz+AKUukVg=</latexit>

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Device Fleet: Distributed Test Bed for AutoTVM

AutoTVMExperiment 1

AutoTVMExperiment 2

Resource Manager (Tracker)

Nvidia GPU Server

RPC RT CUDA

Android Phone

RPC RT OpenCL

Zynq FPGA Board

RPC RT Bitstream

Resource TokenResource

Allocation

Persistent Remote Session

Page 146: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Device Fleet: Distributed Test Bed for AutoTVM

AutoTVMExperiment 1

AutoTVMExperiment 2

Resource Manager (Tracker)

Nvidia GPU Server

RPC RT CUDA

Android Phone

RPC RT OpenCL

Zynq FPGA Board

RPC RT Bitstream

Resource TokenResource

Allocation

Persistent Remote Session

Scale up optimization Resource sharing

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Device Fleet in Action

Page 148: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

TVM: Learning-based Learning System

Why do we need machine learning for systems

How to build intelligent systems with learning

End to end learning-based learning system stack

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AutoTVM

TVM: End to End Deep Learning Compiler

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TVM: End to End Deep Learning Compiler

Optimization

AutoTVM

High-Level Differentiable IR

Tensor Expression and Optimization Search Space

LLVM, CUDA, Metal

AutoTVM

Device Fleet

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TVM: End to End Deep Learning Compiler

Optimization

AutoTVM

High-Level Differentiable IR

Tensor Expression and Optimization Search Space

LLVM, CUDA, Metal

AutoTVM

Device Fleet

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Tensor Expression and Optimization Search Space

Hardware

Based on Halide’s compute/schedule separation

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Tensor Expression and Optimization Search Space

Hardware

C = tvm.compute((m, n), lambda y, x: tvm.sum(A[k, y] * B[k, x], axis=k))

Tensor Expression (Specification)

Based on Halide’s compute/schedule separation

Page 154: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Tensor Expression and Optimization Search Space

Hardware

Search space: Possible mappings from the expression to valid hardware programs

C = tvm.compute((m, n), lambda y, x: tvm.sum(A[k, y] * B[k, x], axis=k))

Tensor Expression (Specification)

Based on Halide’s compute/schedule separation

Page 155: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Tensor Expression and Optimization Search Space

Hardware

Search space: Possible mappings from the expression to valid hardware programs

C = tvm.compute((m, n), lambda y, x: tvm.sum(A[k, y] * B[k, x], axis=k))

Tensor Expression (Specification)

Based on Halide’s compute/schedule separation

What is the search space

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Search Space for CPUs

L1D L1IL2

L3

L1D L1IL2

implicitly managed

Compute Primitives

scalar vector

Memory Subsystem

Loop Transformations

Cache Locality Vectorization

CPUs

Reuse primitives from prior work: Halide, Loopy

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Hardware-aware Search Space

CPUs GPUs TPU-like specialized Accelerators

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Search Space for GPUsCompute Primitives

scalar vector

Memory Subsystem

L2

RF RFTX/L1

SM

RF RFTX/L1

SM

mixed

GPUs

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Search Space for GPUsCompute Primitives

scalar vector

Memory Subsystem

L2

RF RFTX/L1

SM

RF RFTX/L1

SM

mixed

GPUs

Shared memory among compute cores

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Search Space for GPUsCompute Primitives

scalar vector

Memory Subsystem

L2

RF RFTX/L1

SM

RF RFTX/L1

SM

mixed

Thread Cooperation

Use of Shared Memory

GPUs

Shared memory among compute cores

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Search Space for TPU-like Specialized Accelerators

Tensor Compute Primitives

Unified Buffer Acc

FIFO

Explicitly Managed Memory Subsystem

TPUs

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Search Space for TPU-like Specialized Accelerators

Tensor Compute Primitives

Unified Buffer Acc

FIFO

Explicitly Managed Memory Subsystem

TPUs

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Tensorization ChallengeCompute primitives

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Tensorization ChallengeCompute primitives

scalar

Page 165: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Tensorization ChallengeCompute primitives

scalar vector

Page 166: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Tensorization ChallengeCompute primitives

scalar vector tensor

Page 167: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Tensorization ChallengeCompute primitives

scalar vector tensor

Challenge: Build systems to support emerging tensor instructions

Page 168: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Tensorization Challenge

C = tvm.compute((m, n), lambda y, x: tvm.sum(A[k, y] * B[k, x], axis=k))

Computation Specification (Tensor Expression)

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Tensorization Challenge

C = tvm.compute((m, n), lambda y, x: tvm.sum(A[k, y] * B[k, x], axis=k))

Computation Specification (Tensor Expression)

A = tvm.placeholder((8, 8)) B = tvm.placeholder((8,))k = tvm.reduce_axis((0, 8)) C = tvm.compute((8, 8), lambda y, x: tvm.sum(A[k, y] * B[k], axis=k))

HW Interface Specification by Tensor Expression

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Tensorization Challenge

C = tvm.compute((m, n), lambda y, x: tvm.sum(A[k, y] * B[k, x], axis=k))

Computation Specification (Tensor Expression)

A = tvm.placeholder((8, 8)) B = tvm.placeholder((8,))k = tvm.reduce_axis((0, 8)) C = tvm.compute((8, 8), lambda y, x: tvm.sum(A[k, y] * B[k], axis=k))

HW Interface Specification by Tensor Expression

Tensorization

Page 171: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Search Space for TPU-like Specialized Accelerators

Tensor Compute Primitives

Unified Buffer Acc

FIFO

Explicitly Managed Memory Subsystem

TPUs

Page 172: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Search Space for TPU-like Specialized Accelerators

Tensor Compute Primitives

Unified Buffer Acc

FIFO

Explicitly Managed Memory Subsystem

TPUs

Page 173: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Software Support for Latency Hiding

Single Module No Task-Pipelining

load inputs

load weights

matrix multiplication

store outputs

load inputs

load weights

matrix multiplication

store outputsstore outputs

load inputs

load weights

matrix multiplication

store outputs

load inputs

load weights

matrix multiplication

store outputsstore outputs

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Software Support for Latency Hiding

Single Module No Task-Pipelining

load inputs

load weights

matrix multiplication

store outputs

load inputs

load weights

matrix multiplication

store outputs

load inputs

load weights

load inputs

load weights

matrix multiplication

matrix multiplication

store outputs

store outputs

Multiple-Module Task-Level Pipelining

Page 175: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Explicit dependencies managed by software to hide memory latency

Software Support for Latency Hiding

Single Module No Task-Pipelining

load inputs

load weights

matrix multiplication

store outputs

load inputs

load weights

matrix multiplication

store outputs

load inputs

load weights

load inputs

load weights

matrix multiplication

matrix multiplication

store outputs

store outputs

Multiple-Module Task-Level Pipelining

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Software Support for Latency Hiding

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Software Support for Latency Hidingload inputs

load weights

load inputs

load weights

matrix multiplication

matrix multiplication

store outputs

store outputsMulti-threaded

Programvthread0

vthread1

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Software Support for Latency Hidingload inputs

load weights

load inputs

load weights

matrix multiplication

matrix multiplication

store outputs

store outputsMulti-threaded

Programvthread0

vthread1

load inputs

load weights

load inputs

load weights

matrix multiplication

matrix multiplication

store outputs

store outputs

Program with Task-level Pipeline

Instructions

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Summary: Hardware-aware Search Space

C = tvm.compute((m, n), lambda y, x: tvm.sum(A[k, y] * B[k, x], axis=k))

Tensor Expression Language

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Summary: Hardware-aware Search Space

Loop Transformations

Thread Bindings

Cache Locality

Primitives in prior work:Halide, Loopy

C = tvm.compute((m, n), lambda y, x: tvm.sum(A[k, y] * B[k, x], axis=k))

Tensor Expression Language

Page 181: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Summary: Hardware-aware Search Space

Loop Transformations

Thread Bindings

Cache Locality

Primitives in prior work:Halide, Loopy

Thread Cooperation Tensorization Latency

Hiding …New primitives for GPUs, and enable TPU-like Accelerators

C = tvm.compute((m, n), lambda y, x: tvm.sum(A[k, y] * B[k, x], axis=k))

Tensor Expression Language

Page 182: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Summary: Hardware-aware Search Space

Hardware

Loop Transformations

Thread Bindings

Cache Locality

Primitives in prior work:Halide, Loopy

Thread Cooperation Tensorization Latency

Hiding …New primitives for GPUs, and enable TPU-like Accelerators

C = tvm.compute((m, n), lambda y, x: tvm.sum(A[k, y] * B[k, x], axis=k))

Tensor Expression Language

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TVM: End to End Deep Learning Compiler

Optimization

AutoTVM

High-Level Differentiable IR

Tensor Expression and Optimization Search Space

LLVM, CUDA, Metal

AutoTVM

Device Fleet

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End to End Inference Performance (Nvidia Titan X)

TensorFlow-XLA

Apache MXNetTensorFlow

ResNet-18 MobileNet0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

Tim

e(m

s)

LSTM LM DQN DCGAN0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

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End to End Inference Performance (Nvidia Titan X)

TensorFlow-XLA

Apache MXNetTensorFlow

ResNet-18 MobileNet0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

Tim

e(m

s)

LSTM LM DQN DCGAN0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

Backed by cuDNN

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End to End Inference Performance (Nvidia Titan X)

ResNet-18 MobileNet0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

Tim

e(m

s)

LSTM LM DQN DCGAN0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

TensorFlow-XLA

Apache MXNetTensorFlow TVM

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End to End Inference Performance (Nvidia Titan X)

ResNet-18 MobileNet0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

Tim

e(m

s)

LSTM LM DQN DCGAN0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

TensorFlow-XLA

Apache MXNetTensorFlow TVM

Competitive on standard models

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End to End Inference Performance (Nvidia Titan X)

ResNet-18 MobileNet0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

Tim

e(m

s)

LSTM LM DQN DCGAN0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

TensorFlow-XLA

Apache MXNetTensorFlow TVM

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End to End Inference Performance (Nvidia Titan X)

ResNet-18 MobileNet0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

Tim

e(m

s)

LSTM LM DQN DCGAN0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

TensorFlow-XLA

Apache MXNetTensorFlow TVM

3x better on emerging models

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Portable Performance Across Hardware Platforms

ARM GPU (MALI)ARM CPU(Cortex-A53)

TensorFlow Lite TVMArmComputeLib

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Portable Performance Across Hardware Platforms

ARM GPU (MALI)ARM CPU(Cortex-A53)

TensorFlow Lite TVMArmComputeLib

Special frameworks for the particular hardware platform

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TVM: End to End Deep Learning Compiler

What about Accelerator Support?

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VTA: Open & Flexible Deep Learning Accelerator

Current TVM Stack

Moreau, Chen, et al. work in progress

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VTA: Open & Flexible Deep Learning Accelerator

VTA MicroArchitecture

Current TVM Stack

Moreau, Chen, et al. work in progress

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VTA: Open & Flexible Deep Learning Accelerator

VTA Hardware/Software Interface (ISA)

VTA MicroArchitecture

Current TVM Stack

Moreau, Chen, et al. work in progress

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VTA: Open & Flexible Deep Learning Accelerator

VTA Hardware/Software Interface (ISA)

VTA MicroArchitecture

Current TVM Stack

VTA Runtime & JIT Compiler

Moreau, Chen, et al. work in progress

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VTA: Open & Flexible Deep Learning Accelerator

VTA Hardware/Software Interface (ISA)

VTA MicroArchitecture VTA Simulator

Current TVM Stack

VTA Runtime & JIT Compiler

Moreau, Chen, et al. work in progress

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VTA: Open & Flexible Deep Learning Accelerator• Runtime JIT compile

accelerator micro code

• Support heterogenous devices, 10x better than CPU on the same board.

• Move hardware complexity to software

VTA Hardware/Software Interface (ISA)

VTA MicroArchitecture VTA Simulator

Current TVM Stack

VTA Runtime & JIT Compiler

Moreau, Chen, et al. work in progress

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VTA: Open & Flexible Deep Learning Accelerator• Runtime JIT compile

accelerator micro code

• Support heterogenous devices, 10x better than CPU on the same board.

• Move hardware complexity to software

VTA Hardware/Software Interface (ISA)

VTA MicroArchitecture VTA Simulator}compiler, driver, hardware design full stack open source

Current TVM Stack

VTA Runtime & JIT Compiler

Moreau, Chen, et al. work in progress

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TVM: End to End Deep Learning Compiler

High-Level Differentiable IR

Tensor Expression and Optimization Search Space

LLVM, CUDA, Metal

Optimization

AutoTVM

Device Fleet

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TVM: End to End Deep Learning Compiler

High-Level Differentiable IR

Tensor Expression and Optimization Search Space

LLVM, CUDA, Metal VTA

Edge FPGA

Cloud FPGA ASIC

Optimization

AutoTVM

Device Fleet

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TVM Impact

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TVM ImpactOpen source: 202 contributors from UW, Berkeley, Cornell, UCLA, AWS, Huawei, NTT, Facebook, Qualcomm, …

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TVM ImpactOpen source: 202 contributors from UW, Berkeley, Cornell, UCLA, AWS, Huawei, NTT, Facebook, Qualcomm, …

Used in production

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TVM Enables New Research Frontiers

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TVM Enables New Research Frontiers

• PL: New high-level differentiable programming IR (UW)

Page 207: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

TVM Enables New Research Frontiers

• PL: New high-level differentiable programming IR (UW)

• Architecture: New deep learning ASICs, RISC-V (UW, Cornell)

Page 208: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

TVM Enables New Research Frontiers

• PL: New high-level differentiable programming IR (UW)

• Architecture: New deep learning ASICs, RISC-V (UW, Cornell)

• Security: trusted enclave for private aware ML. (Berkeley)

Page 209: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

TVM Enables New Research Frontiers

• PL: New high-level differentiable programming IR (UW)

• Architecture: New deep learning ASICs, RISC-V (UW, Cornell)

• Security: trusted enclave for private aware ML. (Berkeley)

• Machine learning: test bed for automatic optimization. (UW)

Page 210: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

TVM Enables New Research Frontiers

• PL: New high-level differentiable programming IR (UW)

• Architecture: New deep learning ASICs, RISC-V (UW, Cornell)

• Security: trusted enclave for private aware ML. (Berkeley)

• Machine learning: test bed for automatic optimization. (UW)

Reseach Group

Page 211: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

TVM Enables New Research Frontiers

• PL: New high-level differentiable programming IR (UW)

• Architecture: New deep learning ASICs, RISC-V (UW, Cornell)

• Security: trusted enclave for private aware ML. (Berkeley)

• Machine learning: test bed for automatic optimization. (UW)

Reseach Group

TVM Conference 180 attendees, 20+ talks

Page 212: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Learning Systems

Data science for everyone

Scale up deep learning

Deploy AI everywhere

Page 213: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Learning Systems

Data science for everyone

Scale up deep learning

Deploy AI everywhere

What’s Next

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Learning-based Learning System

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Learning-based Learning System

Application

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Learning-based Learning System

Application

Model MobileNet-V2

Page 217: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Learning-based Learning System

Application

Model MobileNet-V2

Hardware ARM Cortex A53

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Learning-based Learning System

Space of Tensor Programs

Application

Model MobileNet-V2

Hardware ARM Cortex A53

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Learning-based Learning System

AutoTVMSpace of Tensor Programs

Application

Model MobileNet-V2

Hardware ARM Cortex A53

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Learning-based Learning System

AutoTVMSpace of Tensor Programs

Optimized Tensor Program

Application

Model MobileNet-V2

Hardware ARM Cortex A53

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Full Stack Learning-based Learning System

Page 222: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Full Stack Learning-based Learning System

Application

Page 223: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Full Stack Learning-based Learning System

Application

Space of Models

Page 224: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Full Stack Learning-based Learning System

Space of Tensor Programs

Application

Space of Models

Page 225: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Full Stack Learning-based Learning System

Space of Tensor Programs

Application

Space of Models

Space of Hardware Variants

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Full Stack Learning-based Learning System

Full-StackAutoTVM

Space of Tensor Programs

Application

Space of Models

Space of Hardware Variants

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Full Stack Learning-based Learning System

Full-StackAutoTVM

Space of Tensor Programs

Application

Space of Models

Space of Hardware Variants

Optimized Tensor Program

Auto Optimized Net

Auto Hardware Variant

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Full Stack Learning-based Learning System

High-Level Differentiable IR

Tensor Expression IR

LLVM, CUDA VTA

Edge FPGA

Cloud FPGA ASIC

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Full Stack Learning-based Learning System

High-Level Differentiable IR

Tensor Expression IR

LLVM, CUDA VTA

Edge FPGA

Cloud FPGA ASIC

ML Models

System Optimizations

Hardware Design}Full-stack co-design

ML as the first-class citizen

Page 230: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Lifecycle of Intelligent ApplicationsD

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Lifecycle of Intelligent ApplicationsD

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Learning System

Page 232: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Lifecycle of Intelligent ApplicationsD

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Learning System

Model

Page 233: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Lifecycle of Intelligent ApplicationsD

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Learning System

Model

Deploy(serving)

Page 234: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Lifelong Learning Systems

Learning System

Page 235: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Lifelong Learning Systems

Learning System Environment

Page 236: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Lifelong Learning Systems

Learning System Environment

Page 237: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Lifelong Learning Systems

Learning System Environment

New Data

Page 238: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Lifelong Learning Systems

Learning System Environment

New Data

New Models

Page 239: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Lifelong Learning Systems

Learning System Environment

New Data

New Models

New Tasks

Page 240: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Lifelong Learning Systems

Learning System

Lifelong evolution of model, data and system optimizations

Environment

New Data

New Models

New Tasks

Page 241: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Challenges in Lifelong Learning Research

Page 242: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Challenges in Lifelong Learning Research

Net2Net: Accelerating learning via knowledge transfer. Chen, et al. ICLR 16

Model transfer as model complexity grows

Page 243: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Challenges in Lifelong Learning Research

Net2Net: Accelerating learning via knowledge transfer. Chen, et al. ICLR 16

Model transfer as model complexity grows

Smart data acquisition, task prioritization

Page 244: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Challenges in Lifelong Learning Research

Net2Net: Accelerating learning via knowledge transfer. Chen, et al. ICLR 16

Model transfer as model complexity grows

Smart data acquisition, task prioritization

Build real-world learning systems as test beds Learning-based learning systems are ideal starting points

Page 245: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Learning-based Learning Systems

Data science for everyone

Scale up deep learning

Deploy AI everywhere

Page 246: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Learning-based Learning Systems

Data science for everyone

Scale up deep learning

Deploy AI everywhere

Full-stack Learning-based Learning System

Page 247: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Learning-based Learning Systems

Data science for everyone

Scale up deep learning

Deploy AI everywhere

Full-stack Learning-based Learning System

Life-long learning Systems

Page 248: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Take Aways: Elements of Future Learning Systems

Beside being accessible and scalable

Intelligent automated by machine learning

Full stack model, systems and hardware co-design

Lifelong consider the entire life-cycle of learning

Page 249: TVM: Learning-based Learning Systems...2010 2006 Public Cloud ML Systems MTurk Based on personal view Abbreviated History of Machine Learning 1960 Data Hardware LSTM Research ConvNet

Take Aways: Elements of Future Learning Systems

Beside being accessible and scalable

Intelligent automated by machine learning

Full stack model, systems and hardware co-design

Lifelong consider the entire life-cycle of learning