An energy-efficient deep convolutional neural network accelerator featuring conditional computing and low external memory access

Minkyu Kim, Jae Sun Seo

Research output: Contribution to journalArticlepeer-review

Abstract

With its algorithmic success in many machine learning tasks and applications, deep convolutional neural networks (DCNNs) have been implemented with custom hardware in a number of prior works. However, such works have not exploited conditional/approximate computing to the utmost toward eliminating redundant computations of CNNs. This article presents a DCNN accelerator featuring a novel conditional computing scheme that synergistically combines precision cascading (PC) with zero skipping (ZS). To reduce many redundant convolutions that are followed by max-pooling operations, we propose precision cascading, where the input features are divided into a number of low-precision groups and approximate convolutions with only the most significant bits (MSBs) are performed first. Based on this approximate computation, the full-precision convolution is performed only on the maximum pooling output that is found. This way, the total number of bit-wise convolutions can be reduced by ∼ 2× with < 0.8% degradation in ImageNet accuracy. PC provides the added benefit of increased sparsity per low-precision group, which we exploit with ZS to eliminate the clock cycles and external memory accesses. The proposed conditional computing scheme has been implemented with custom architecture in a 40-nm prototype chip, which achieves a peak energy efficiency of 24.97 TOPS/W at 0.6-V supply and a low external memory access of 0.0018 access/MAC with VGG-16 CNN for ImageNet classification and a peak energy efficiency of 28.51 TOPS/W at 0.9-V supply with FlowNet for Flying Chair data set.

Original languageEnglish (US)
Article number9229157
Pages (from-to)803-813
Number of pages11
JournalIEEE Journal of Solid-State Circuits
Volume56
Issue number3
DOIs
StatePublished - Mar 2021

Keywords

  • Application-specific integrated circuit (ASIC)
  • approximate computing
  • conditional computing
  • deep convolutional neural network (DCNN)
  • deep learning
  • energy-efficient accelerator

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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