Interchangeable Hebbian and Anti-Hebbian STDP Applied to Supervised Learning in Spiking Neural Network

Che Chia Chang, Pin Chun Chen, Boris Hudec, Po-Tsun Liu, Tuo-Hung Hou

研究成果: Conference contribution同行評審

3 引文 斯高帕斯(Scopus)

摘要

This work provides a complete framework, including device, architecture, and algorithm, for implementing bio-inspired supervised spiking neural networks (SNNs) on hardware. An analog synapse with atypical dual bipolar resistive-switching (D-BRS) modes demonstrates interchangeable Hebbian spiking-timing-dependent plasticity (STDP) and anti-Hebbian STDP, and it is capable of implementing supervised ReSuMe SNNs in crossbar arrays. By using an 'exchange' update scheme, accurate supervised learning (∼96% for MNIST) is achieved in a compact network.

原文English
主出版物標題2018 IEEE International Electron Devices Meeting, IEDM 2018
發行者Institute of Electrical and Electronics Engineers Inc.
頁面15.5.1-15.5.4
ISBN(電子)9781728119878
DOIs
出版狀態Published - 16 1月 2019
事件64th Annual IEEE International Electron Devices Meeting, IEDM 2018 - San Francisco, United States
持續時間: 1 12月 20185 12月 2018

出版系列

名字Technical Digest - International Electron Devices Meeting, IEDM
2018-December
ISSN(列印)0163-1918

Conference

Conference64th Annual IEEE International Electron Devices Meeting, IEDM 2018
國家/地區United States
城市San Francisco
期間1/12/185/12/18

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