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Workshop Session_KETI

Binarized Neural Network Processor for Residual Network

원문정보

Jeahack Lee, Hyeonseong Kim, Junwon Jeong, Kyeongmook Oh, Byung-Soo Kim

한국차세대컴퓨팅학회 한국차세대컴퓨팅학회 학술대회 The 9th International Conference on Next Generation Computing 2023 2023.12 pp.27-28
피인용수 : 0건 (자료제공 : 네이버학술정보)

초록

영어

This paper proposes a binarized neural network (BNN) processor supporting residual networks. The processor was fabricated in UMC 40-nm CMOS technology. Test results show that performance and energy efficiency are 1036.8 GOPS, and 66.5 GOPS/mW at 200MHz, respectively.

목차

Abstract
I. INTRODUCTION
II. PROPOSED BINARIZED RESNETE DATA FLOW
III. PROPOSED BNN ARCHITECTURE
IV. IMPLEMENTATION AND RESULTS
V. CONCLUSION
ACKNOWLEDGMENT
REFERENCES

키워드

저자정보

  • Jeahack Lee SoC Platforrm Research Center, Korea Electronics Technology Institute
  • Hyeonseong Kim SoC Platforrm Research Center, Korea Electronics Technology Institute
  • Junwon Jeong Dept. of Electronics Engineering Sookmyung Women’s University
  • Kyeongmook Oh SoC Platforrm Research Center, Korea Electronics Technology Institute
  • Byung-Soo Kim SoC Platforrm Research Center, Korea Electronics Technology Institute

참고문헌

자료제공 : 네이버학술정보

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