Alternative Computing Technologies

Alternative Computing Technologies

CS 8803 ACTSpring 2014

Hadi [email protected] Georgia Institute of Technology

mailto:[email protected]

2

Hadi EsmaeilzadehFrom Khoy, Iran

3

PhD in CSE, University of WashingtonDoug Burger and Luis Ceze

2013 William Chan Memorial Dissertation Award

MSc in CS, The University of Texas at AustinMSc and BSc in ECE, University of Tehran

4

Research: ACT LabAlternative Computing Technologies

General-purpose approximate computing Bridging neuromorphic and von Neumann

models of computing Analog computing System design for online machine learning System design for perpetual devices

5

Agenda

1. Who is Hadi

2. Course organization3. Why alternative computing technologies

1. How we became and industry of new possibilities2. Why we might become an industry of replacement

4. Possible alternative computing technologies5. Quiz # 1

6

Objective

Explore cutting-edge research on new and alternative paradigms of computing

Empower you with higher order critical thinking

Improve your technical writing and speaking Innovate in alternative computing

technologies

7

Format

Seminar course– Reading papers– Critiquing and discussing the papers– Brainstorming about new ideas– Developing new technologies

Mostly your presentations– I will only lecture three times

8

Grading rubric

Component FractionClass Presentation 30%Class Participation 10%Critiques 25%Final Project 35%

9

Class presentation

Objective: Communicate and analyze ideas

4 points: Clearly presenting the key ideas 1 points: Clear, well-organized slides 5 points: Stimulating interesting discussion 1 point bonus

10

Class participation

You have to say somethinginteresting!

By 9pm the night before, two comments/questions – Your new ideas– Critical questions about methodologies and conclusions– Why will the paper be cite– What you learned– Main insights from the papers

11

Critiques

Objective: developing high-order critical thinking

Summary (quarter a page) Strengths (1-3 sentences) Weaknesses (1-3 sentences) Analysis I (1 paragraph) Analysis II (1 paragraph)

Please read the “The task of the referee by Alan Jay Smith”

Reading material for writing critiquesThe task of the referee

Allen Jay SmithStyle: The Basics of Clarity and Grace

Joseph M. Williams

13

Final project

Groups of two Options

– Implementing a new idea– Extending an existing paper– Re-implement a paper– Survey at least ten papers

Evaluation– Implementation– Writing– Oral presentation

14

Prerequisites

Understand a subset of– VLSI Circuits– Computer architecture– Programming Languages– Machine learning

Do– Programming

15

Agenda

1. Who Hadi is2. Course organization

3. Why alternative computing technologies

1. How we became and industry of new possibilities2. Why we might become and industry of replacement


16

What has made computing pervasive? What is the backbone

of computing industry?

17

Programmability Networking

18

What makes computers programmable?

19

von Neumann architectureGeneral-purpose processors

Components – Memory (RAM)– Central processing unit (CPU)

• Control unit• Arithmetic logic unit (ALU)

– Input/output system Memory stores program and data Program instructions execute sequentially

20

Programmability versus Efficiency

21

Programmability versus Efficiency

Efficiency

Prog

ram

mab

ility

General-Purpose Processors

FPGAsASICs

GPUs

SIMD Units

22

What is the difference between the computing industry and the paper towel

industry?

23

Industry of new possibilities

Industry of replacement

1971 2013

?

24

Can we continue being an industry of new possibilities?

Personalizedhealthcare

Virtualreality

Real-timetranslators

25

Agenda

1. Who Hadi is2. Course organization3. Why alternative computing technologies

1. How we became and industry of new possibilities

2. Why we might become and industry of replacement


26

Moore’s LawOr, how we became an industry of new possibilities

Double the number of transistors Build higher performance

general-purpose processors– Make the transistors available to masses– Increase performance (1.8×↑)– Lower the cost of computing (1.8×↓)

Every 2 Years

27

What is the catch?Powering the transistors without melting the chip

1970 1975 1980 1985 1990 1995 2000 2005 2010 20150

1

100

10,000

1,000,000

100,000,000

10,000,000,000

0.5

130

2300

2200000000Chip Transistor CountChip Power

Moore’s Law

W

W

28

Dennard scaling: Doubling the transistors; scale their power down

Dimensions

Voltage

DopingConcentrations

×0.7

Area 0.5×↓

Power 0.5×↓

Frequency 1.4×↑

Capacitance 0.7×↓

Transistor: 2D Voltage-Controlled Switch

Power = Capacitance × Frequency × Voltage2

29

Dennard scaling broke: Double the transistors; still scale their power down

Dimensions

Voltage

DopingConcentrations

×0.7

Area 0.5×↓

Power 0.5×↓

Frequency 1.4×↑

Capacitance 0.7×↓

Transistor: 2D Voltage-Controlled Switch

Power = Capacitance × Frequency × Voltage2

30

Dark siliconIf you cannot power them, why bother making them?

Area 0.5×↓Power 0.5×↓

Fraction of transistors that need to bepowered off at all times

due to power constraints

Dark Silicon

31

Looking backEvolution of processors

1971 2003

Single-core Era

2004

2013

Multicore Era

Dennard scalingbroke

740 KHz

3.4 GHz 3.5 GHz

32

Are multicores a long-term solution or just a stopgap?

33

Agenda


1. How we became and industry of new possibilities

2. Why we might become an industry of replacement


34

Modeling future multicoresQuantify the severity of the problem

Predict the performance of best-case multicores– From 45 nm to 8 nm– Parallel benchmarks– Fixed power and area budget

Transistor Scaling Model

Single-CoreScaling Model

MulticoreScaling Model

Esmaeilzadeh, Belem, St. Amant, Sankaralingam, Burger, “Dark Silicon and the End of Multicore Scaling,” ISCA 2011

Transistor scaling modelFrom 45 nm to 8 nm

35

Area

Power

Speed

[ITRS, 2010]

OptimisticScaling Model

32× ↓

8.3× ↓

3.9× ↑

[VLSI-DAT, 2010]

Conservative Scaling Model

32× ↓

4.5× ↓

1.3× ↑

[Dennard, 1974]

HistoricalScaling

32× ↓

32× ↓

5.7× ↑

36

Single-core model (45 nm)

0 5 10 15 20 25 30 35 400

5

10

15

20

25

30Intel NehalemAMD ShanghaiIntel CoreIntel AtomPareto Frontier (45 nm)

Core Performance (SPECmark)

Core

Pow

er (W

atts)

Power-Performance and Area-PerformancePareto Optimal Frontiers

37

Single-core scaling model

0 5 10 15 20 25 30 35 400

5

10

15

20

25

30

Core Performance (SPECmark)

Core

Pow

er (W

atts)

Single-core Scaling Model:Single-core Model × Transistor Scaling Model

From 45 nm to 8 nm

Transistor Speed Scaling Factor

Transistor Power Scaling Factor

38

Exhaustive search of multicore design space(Examine 800 design points for every technology node)

Multicore scaling model

Single Core Search Space(Scaled Area and Power Pareto Frontiers)

From 45 nm to 8 nm

Application Characteristics(% Parallel, % Memory Accesses)

Constraints(Area and Power Budget)

Microarchitectural Features(Cache and Memory Latencies, CPI,

Memory Bandwidth)

Multicore Topology(Symmetric, Asymmetric, Dynamic, Composable)

Multicore Organization: CPU-Like, GPU-Like

(# of HW Threads, Cache Sizes)

39

Multicore model (Amdahl’s Law)

40

Dark silicon

40%

41

Evaluation Setup

Applications:– 12 PARSEC Parallel Benchmarks

Baseline:– The best multicore design available at 45 nm

Constraints:– Driven from the best multicore design at 45 nm

• Fixed Power Budget: 125 W• Fixed Area Budget: 111 mm2

42Dark

Sili

con 45 nm 32 nm 22 nm 16 nm 11 nm 8 nm

45 nm 32 nm 22 nm 16 nm 11 nm 8 nm0

4

8

12

16

20

Historical Trend

Optimistic Transistor Scaling (Projection)

Conservative Transistor Scaling (Projection)

Perf

orm

ance

Impr

ovem

ent /

45

nm

10 years

1% 17% 36% 40% 51%

18×

3.7×

2013

7.9×

43

Industry of replacement?

Multicores are likely to be a stopgap– Not likely to continue the historical trends– Do not overcome the transistor scaling trends– The performance gap is significantly large

Radical departures from conventional approaches are necessary– Extract more performance and efficiency from silicon

while preserving programmability– Explore other sources of computing

44

Agenda


1. How we became and industry of new possibilities2. Why we might become and industry of replacement

4. Possible alternative computing technologies

5. Quiz # 1

45

Alternative computing technologies

Approximate Computing

Perpetual Computing

Analog Computing

Stochastic Computing Human-basedComputing

Biological Computing

Neuromorphic Computing

46

Approximate computingEmbracing error

Relax the abstraction of near-perfect accuracy in general-purpose computing

Allow errors to happen in the computation– Run faster– Run more efficiently

47

48

New landscape of computingPersonalized and targeted computing

49

Classes ofapproximate applications Programs with analog inputs– Sensors, scene reconstruction

Programs with analog outputs– Multimedia

Programs with multiple possible answers– Web search, machine learning

Convergent programs– Gradient descent, big data analytics

50

Adding a third dimensionEmbracing Error

Erro

r

51

A fertile ground for innovationEr

ror

52

Approximate computing techniques

Same Model• Sampling– Loop perforation (MIT)

• Compression– Sage (Michigan)

• Early termination– Green (MSR)

• Replacement– Green (MSR)

• Lower voltage– Truffle (Rice, UW)

From Model to Model• von Neumann to Neural

– NPUs (UW, GaTech)

53

Analog ComputingComputing with Physics

http://youtu.be/dAyDi1aa40E

http://youtu.be/dAyDi1aa40E

54

Agenda


1. How we became and industry of new possibilities2. Why we might become an industry of replacement

4. Possible alternative computing technologies

5. Quiz # 1

55

Documents

Alternative Computing Technologies