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Efficient FPGA Stream Folding: A Solution for Acceleration

This paper discusses stream programming on FPGAs, focusing on stream folding to maximize throughput while meeting area and latency constraints. Opportunities, problems, and results are presented.

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Efficient FPGA Stream Folding: A Solution for Acceleration

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  1. A Computing Origami: Folding Streams in FPGAs DAC 2009, California, USA S. M. Farhad PhD Student University of Sydney

  2. Outline • Motivation • Stream programming • FPGA • Problem • Stream Folding • Results • Conclusion 2

  3. Stream Programming Paradigm • Programs expressed as stream graphs • Streams: Sequence of data elements • Actor: Functions applied to streams • Independent actors with explicit communication • Regular and repeating computation Streams Actor/Filter Streams

  4. FPGA • FPGAs are widely available as programmable coprocessors • Opportunities to exploit FPGA-based acceleration • Multimedia, networking, graphics, and security codes

  5. Problem • Maximizing throughput subject to • Area and latency constraints • Resolving bottleneck actors • The replicated filters do not require resynthesis

  6. Motivating Example

  7. Motivating Example

  8. Motivating Example

  9. Outline • Motivation • Stream programming • FPGA • Problem • Stream Folding • Results • Conclusion 9

  10. Area/Throughput Design Folding 1 foreachFilter f in S do 2 workFactor[f] = f.latency.S.runs(f); 3 designPointArea+ = f.area.workFactor[f]; 4 scaleLimit = minf.hasState (1/workFactor[f]); 5 scaling = min(AREA/designPointArea, scaleLimit); 6 foreachFilter f in S do 7 replication[f] = workFactor[f].scaling; 8 whilearea(replication) > AREA do 9 replication = reduceThroughput(replication);

  11. Calculating Throughput

  12. Calculating Latency • FPGAs that are coupled to host processors • Initiation interval (DMA) • Replication improves throughput, it often increases the latency! • Major factors for latency variation • Non-periodic data arrival • Data-token reordering • Local congestion

  13. Latency constrained design folding 1 latConf= null ; T = ∞; 2 whilethroughput(thrConf) ≤ T do 3 iffeasibleImprovement(thrConf) then 4 candidates = simAnnealing(thrConf, T); 5 foreachcandidate in candidates do 6 ifthroughput(candidate) < Tthen 7 latConf = candidate; 8 T = throughput(latConf); 9 thrConf = reduceThroughput(thrConf); 10 returnlatConf

  14. Results

  15. Questions?

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