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Mitigating Asymmetric Nonlinear Weight Update Effects in Hardware Neural Network based on Analog Resistive Synapse by Chang, Chih-Cheng; Chen, Pin-Chun; Chou, Teyuh; Wang, I-Ting; Hudec, Boris; Chang, Che-Chia; Tsai, Chia-Ming; Chang, Tian-Sheuan; Hou, Tuo-Hung is a scholarly article available to read on EtoBox.

What is Mitigating Asymmetric Nonlinear Weight Update Effects in Hardware Neural Network based on Analog Resistive Synapse about?

Asymmetric nonlinear weight update is considered as one of the major obstacles for realizing hardware neural networks based on analog resistive synapses because it significantly compromises the online training capability. This paper provides new solutions to this critical issue through co-optimization with the hardware-applicable deep-learning algorithms. New insights on engineering activation functions and a threshold weight update scheme effectively suppress the undesirable training noise induced by inaccurate weight update. We successfully trained a two-layer perceptron network online and improved the classification accuracy of MNIST handwritten digit dataset to 87.8/94.8% by using 6-bit/8-bit analog synapses, respectively, with extremely high asymmetric nonlinearity.

Author

Chang, Chih-Cheng; Chen, Pin-Chun; Chou, Teyuh; Wang, I-Ting; Hudec, Boris; Chang, Che-Chia; Tsai, Chia-Ming; Chang, Tian-Sheuan; Hou, Tuo-Hung

Published

2017

Language

EN