A Low-Power Reconfigurable DSP System

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A Low-Power Reconfigurable DSP System

Marlene Wan, Martin Benes, Arthur AbnousJan Rabaey

EECS Department, University of California, Berkeley

Abstract

Reconfigurable architectures has emerged to be a promising implementation platform to provideflexibility, high-performace and low-power for future wireless embedded devices. We discuss indetail an reconfigurable architecture template and a set of software tools to perform automaticmapping and performance prediction from algorithm to the architecture. We present results ondigital signal processing and wireless communication algorithms to show the effectiveness of thesystem in achieving energy efficiency.

1. Motivation and Background

Future wireless multimedia computing devices are required to adapt their functionality to the

changing parameters of the communication link available at a given time (i.e., bandwidth, error

rates, protocols, etc.). Therefore, these devices have to be flexible enough to accommodate a

various multimedia services (e.g., different video decompression schemes) and communication

capabilities (e.g., cellular GSM, PCS, pico-cellular). At the same time, low-power consumption

will continue to be the predominant design challege of wireless systems. Reconfigurable

architectures has emerged to be a promising implementation platform to provide flexibility, high-

performance [ref] and low-power [ref] [ref] for future wireless embedded devices. In

[Abnous96], a reconfigurable architecture template is proposed to meet both the flexibility and

low-power requirement. In this paper, we will introduce a realization of such architecture

template (in particular, its model of computation and basic processing elements for data-flow


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computations) and supporting software to assist direct implementations on such architecture. The

shaded box in Figure 1 shows the scope this paper covers. The energy efficiency of the proposed

realization is then demonstrated by mapping wireless communication and signal processing

algorithms to the architecture.

Figure 1.

2. Architecture Description

The basic idea behind the proposed architecture is illustrated in Figure. 1. Control flow computa-

tion in performed on the microprocessor and dataflow computation is performed on the satellites.

The architecture template fixes the communication scheme between each satellite as well as the

interface method between the microprocessor and the satellite. Communications between each

satellite is data-flow driven and each satellite also follows strict execution (i.e. operation starts

only when all input datas are ready). Dedicated links are established between satellites.

Kernel* Computation

Mapping

Estimation

Architecture

Desription

Reconfigurable Architecture ImplementationOptimization

AlgorithmOptimization

To architecture selection:

HardwareComponents

*Kernel-computational Intensive operations, often corresponds to datafow computations in nested loops


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In the current realization of the architecture, the satellites are medium to fine grain according to

the definition of [Bart]. The fuctionality of the satellites are divided into three catagories: source,

computation and memory. To support adaptive computations without reconfiguration such as

changing the vector length or number of taps for the computation satellites, a minimum-overhead

mechanism to pass data structure (scalar, vector and matrix) is developed. Each computation

satellite needs to be configured to the data structure it consumes and produces (vectors to scalar

for MAC, for example). The source satellites generate tokens indicating the end of the data

structure along with corresponding data.

Talk about dedicated links between satellites and data steering elements- Three categories: static

(data goes in a fixed direction in-between reconfiguration periods), statically scheduled (data

goes in directions instructed by programs configured at reconfiguration times), dynamically (data

is equipped with the direction) determined. The first two are supported by the current realization

of the architecture template.

The current implementation of the data driven computation scheme is globally asynchronous and

locally synchronous clocking. address generator and inport (with data from microprocessor) and

FPGA can serve as sources. Reconfigurable interconnect [Zhang].

3. The Software Tools

In order to supply fast implementation feedback to the user, tools are developed to support

application specific simulation and direct-mapped synthesis from a high-level language to the

satellites.


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3.A. Simulation Tool

Based on the realization of the architecture template, a simulation environment is developed to

provide application specific simulator in a style similar to [Bart].

Since compution is mapped to clusters of satellites, an object-oriented intermediate form based

on the concept of modules (heterogeneous satellites) and queues (links between satellites) is

created. A mapped kernel is constructed by building a netlist using the module and queue library

(Figure 1). In order to facilitate verification and performance feedback, wrappers are placed

around all modules and queues so modules can be modeled as concurrent processes and queue as

synchronized objects. Energy and time stamps are also associated with each modules and queues

so performance can be collected. A application specific simulator is automatically instantiated

once a netlist is specified.

Currently, the intermediate form is implemented in the C++ language and the Solaris thread

library [26] (other common thread libraries can be switched in easily). Common satellite


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processors (such as MAC/multiply processor, ALU processor, memory and address generator

etc.) have been incorporated in our module library.

3.B. Synthesis Tool

To ease the process of manually mapping algorithms to the architecture, a synthesis tool is

provided to translate an algorithm (specified in a subset of C) to the direct-mapped

implementation of the architecture. The output is the computation specified in the intermediate

form, the kernel performance and energy can then be dynamically collected. For algorithms with

loops with constant loop length, energy and performance information is also analyzed statically

to avoid the overhead of simulation.

The algorithm is compiled to SUIF intermediate form then converted to hierarchical Control

Data Flow Graph (CDFG [Hyper]). The current conversion from SUIF to CDFG exposes all

scalar dependencies but preserve all WAW, RAW, WAW dependencies in array access. The

current mapping allocates arrays of the same name to a particular memory and each operation

node in CDFG to a hardware unit.

Generation of data steering element and address generator is based on the nested loops.

Statically performance estimation for loops with known loop length is also done.

3.C. Orthogonalization as an example


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4. Case Studies

All satellite modules are characterized. Interconnect are characterized also in [Zhang98].

Preliminary overhead of steering element is added. Low energy feature of the system. Allows

architecture selection and serves as the basis of future optimizations

4.A. Multiuser Detection Channel Estimator

Synthesis and performance is determined statically and verified dynamically using the simulator.

Architecture Power mW

TMS320C54x 460 * ref ref

Pleiades 18.04

ASIC 3 ref

4.B. VSELP Speech CODEC

All kernels synthesized, simulated and performance gathered.

Dot_product, FIR, IIR, VectorSumScalarMul, Compute_Code, Covariance_Matrix_Compute.

5. Conclusion

We have presented a low-power reconfigurable multiprocessor system. Future work will include

software level transformation (loop transformation and parallelism), implementation

optimization and more application mappings in the wireless communication domain.

6. References


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G. R. Goslin, A Guide to Using Field Programmable Gate Arrays for Application Specific

Digital Signal Processing Performance, Proceedings of SPIE, vol. 2914, p321-331.

Abnous et al, Evaluation of a Low-Power Reconfigurable DSP Architecture, Proceedings

of the Reconfigurable Architecture Workshop, Orlando, Florida, USA, March 1998.

M. Goel and N. R. Shanbhag, Low-Power Reconfigurable Signal Processing via Dynamic

Algorithm Transformations (DAT), Proceedings of Asilomar Conference on Signals,

Systems and Computers , Pacific Grove, CA, November, 1998.

Gerson and M. Jasiuk, Vector Sum Excited Linear Prediction (VSELP) Speech Coding at

8Kbps, Proceedings of the International Conference on Acoustics, Speech, and Signal

Processing, pp. 461-464, April 1990.

K. Ueda, et al. , Multimedia Complex on a Chip, ISSCC Digest of Technical Papers , pp.

28-29, February 1993.

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A Low-Power Reconfigurable DSP System