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Serving Recurrent Neural Networks Efficiently with a Spatial Accelerator (1909.13654v1)

Published 26 Sep 2019 in cs.DC, cs.LG, and cs.PF

Abstract: Recurrent Neural Network (RNN) applications form a major class of AI-powered, low-latency data center workloads. Most execution models for RNN acceleration break computation graphs into BLAS kernels, which lead to significant inter-kernel data movement and resource underutilization. We show that by supporting more general loop constructs that capture design parameters in accelerators, it is possible to improve resource utilization using cross-kernel optimization without sacrificing programmability. Such abstraction level enables a design space search that can lead to efficient usage of on-chip resources on a spatial architecture across a range of problem sizes. We evaluate our optimization strategy on such abstraction with DeepBench using a configurable spatial accelerator. We demonstrate that this implementation provides a geometric speedup of 30x in performance, 1.6x in area, and 2x in power efficiency compared to a Tesla V100 GPU, and a geometric speedup of 2x compared to Microsoft Brainwave implementation on a Stratix 10 FPGA.

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Authors (3)
  1. Tian Zhao (15 papers)
  2. Yaqi Zhang (20 papers)
  3. Kunle Olukotun (35 papers)
Citations (16)

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