Energy-Efficient Design of Processing Element for Convolutional Neural Network

Yeongjae Choi, Dongmyung Bae, Jaehyeong Sim, Seungkyu Choi, Minhye Kim, Lee Sup Kim

Research output: Contribution to journalArticlepeer-review

23 Scopus citations

Abstract

Convolutional neural network (CNN) is the most prominent algorithm for its wide usage and good performance. Despite the fact that the processing element (PE) plays an important role in CNN processing, there has been no study focusing on PE design optimized for state-of-the-art CNN algorithms. In this brief, we propose an optimal PE implementation including a data representation scheme, circuit block configurations, and control signals for energy-efficient CNN. To validate the excellence of this brief, we compared our proposed design with several previous methods, and fabricated a silicon chip. The software simulation results demonstrated that we can reduce 54% of data bit lengths with negligible accuracy loss. Our optimization on PE achieves to save computing power up to 47%, and an accelerator exploiting our method shows superior results in terms of power, area, and external DRAM access.

Original languageEnglish
Article number7893765
Pages (from-to)1332-1336
Number of pages5
JournalIEEE Transactions on Circuits and Systems II: Express Briefs
Volume64
Issue number11
DOIs
StatePublished - Nov 2017

Keywords

  • convolutional neural network
  • neural network processor
  • Neuromorphic computing
  • processing element

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