標題: Interchangeable Hebbian and Anti-Hebbian STDP Applied to Supervised Learning in Spiking Neural Network
作者: Chang, Che-Chia
Chen, Pin-Chun
Hudec, Boris
Liu, Po-Tsun
Hou, Tuo-Hung
電子工程學系及電子研究所
光電工程學系
Department of Electronics Engineering and Institute of Electronics
Department of Photonics
公開日期: 1-一月-2018
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 (similar to 96% for MNIST) is achieved in a compact network.
URI: http://hdl.handle.net/11536/151104
ISSN: 2380-9248
期刊: 2018 IEEE INTERNATIONAL ELECTRON DEVICES MEETING (IEDM)
顯示於類別:會議論文