A 90nm 103.14 TOPS/W binary-weight spiking neural network CMOS ASIC for real-time object classification

Po Yao Chuang, Pai Yu Tan, Cheng Wen Wu, Juin Ming Lu

研究成果: Conference contribution

摘要

This paper introduces a low-power 90nm CMOS binary weight spiking neural network (BW-SNN) ASIC for real-time image classification. The chip maximizes data reuse through systolic arrays that house the entire 5-layer BW-SNN, requiring a minimum off-chip bandwidth for data access. The chip achieves 97.57% accuracy for real-time bottled-drink recognition, consuming only 0.62uJ per inference. For comparison purpose, it achieves 98.73% accuracy for MNIST hand-written character recognition, consuming only 0.59uJ per inference. The bottled-drink recognition is demonstrated at 300 fps that is well enough for many other real-time applications. The peak efficiency point is 103.14TOPS/W at a voltage of 0.6V, which outperforms other designs so far as we know. By normalizing to the 28nm technology node, the proposed ASIC is about 5× more efficient and 7× lower hardware cost as compared with the state-of-the-art designs.

原文English
主出版物標題2020 57th ACM/IEEE Design Automation Conference, DAC 2020
發行者Institute of Electrical and Electronics Engineers Inc.
ISBN(電子)9781450367257
DOIs
出版狀態Published - 2020 七月
事件57th ACM/IEEE Design Automation Conference, DAC 2020 - Virtual, San Francisco, United States
持續時間: 2020 七月 202020 七月 24

出版系列

名字Proceedings - Design Automation Conference
2020-July
ISSN(列印)0738-100X

Conference

Conference57th ACM/IEEE Design Automation Conference, DAC 2020
國家United States
城市Virtual, San Francisco
期間20-07-2020-07-24

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Control and Systems Engineering
  • Electrical and Electronic Engineering
  • Modelling and Simulation

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