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      • Department of Computer Engineering
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      Energy efficient boosting of GEMM accelerators for DNN via reuse

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      Author(s)
      Cicek, Nihat Mert
      Shen, Xipeng
      Özturk, Özcan
      Date
      2022-06-06
      Source Title
      ACM Transactions on Design Automation of Electronic Systems
      Print ISSN
      1084-4309
      Electronic ISSN
      1557-7309
      Publisher
      Association for Computing Machinery, Inc
      Volume
      27
      Issue
      5
      Pages
      1 - 26
      Language
      English
      Type
      Article
      Item Usage Stats
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      Abstract
      Reuse-centric convolutional neural networks (CNN) acceleration speeds up CNN inference by reusing computations for similar neuron vectors in CNN’s input layer or activation maps. This new paradigm of optimizations is, however, largely limited by the overheads in neuron vector similarity detection, an important step in reuse-centric CNN. This article presents an in-depth exploration of architectural support for reuse-centric CNN. It addresses some major limitations of the state-of-the-art design and proposes a novel hardware accelerator that improves neuron vector similarity detection and reduces the energy consumption of reuse-centric CNN inference. The accelerator is implemented to support a wide variety of neural network settings with a banked memory subsystem. Design exploration is performed through RTL simulation and synthesis on an FPGA platform. When integrated into Eyeriss, the accelerator can potentially provide improvements up to 7.75× in performance. Furthermore, it can reduce the energy used for similarity detection up to 95.46%, and it can accelerate the convolutional layer up to 3.63× compared to the software-based implementation running on the CPU.
      Keywords
      Reuse
      Deep neural networks
      Gemm
      Accelerator
      Permalink
      http://hdl.handle.net/11693/111730
      Published Version (Please cite this version)
      https://dx.doi.org/10.1145/3503469
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      • Department of Computer Engineering 1561
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