Earticle

초록

영어

Spiking neural networks (SNN), employing eventbased spike computation, can be implemented in hardware where on-chip learning and inference are supported in a powerand area-efficient manner. Although many SNN hardware have been proposed for energy-efficient designs using relatively shallow networks, SNN algorithms that support multi-layer learning need to be implemented in hardware to handle more complex datasets. However, multi-layer learning requires more complicated functions like softmax activation, which makes energy-efficient hardware design difficult. In this paper, we present a zero-spike prediction method to skip the complicated function in the convolution layer. Decomposing the original algorithm, the proposed method skips at least 76.90% of softmax activation operations without classification accuracy degradation.

목차

Abstract
I. INTRODUCTION
II. PRELIMINARIES
A. SNN
B. STDP-Based Multi-layer Learning Algorithm
III. PROPOSED ZERO-SPIKE PREDICTION
IV. EXPERIMENTAL RESULT
V. CONCLUSION
ACKNOWLEDGMENT
REFERENCES

키워드

저자

• Hyeonseong Kim [ SoC Platform Research Center, Korea Electronics Technology Institute ]
• Byung-Soo Kim [ SoC Platform Research Center, Korea Electronics Technology Institute ]
• Taeho Hwang [ SoC Platform Research Center, Korea Electronics Technology Institute ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국차세대컴퓨팅학회 학술대회

• 간기

  반년간

• 수록기간

  2021~2025

• 십진분류

  KDC 566 DDC 004