Enhance the robustness to time dependent variability of ReRAM-based neuromorphic computing systems with regularization and 2R synapse

Conference Paper

Time Dependent Variability (TDV) is one of the major concerns in implementing a Neuromorphic Computing System (NCS) with Resistive Random Access Memory (ReRAM). In this work, we propose a variation-distribution aware training algorithm to enhance the robustness of NCS to TDV without incurring extra hardware overhead by leveraging algorithm-level regularization and hardware-level 2R synapse structure. Simulation results on image recognition tasks show that our method improves the system accuracy by up to ~4% and ~10% under the worst-case TDV condition for MNIST and CIFAR-10, respectively. Detailed analysis also shows that our method allows the NCS to use synapses with higher resistance than conventional design for the same accuracy requirement, introducing potential energy saving.

Full Text

Duke Authors

Cited Authors

  • Zheng, Q; Kang, J; Wang, Z; Cai, Y; Huang, R; Li, B; Chen, Y; Li, H

Published Date

  • January 1, 2019

Published In

Volume / Issue

  • 2019-May /

International Standard Serial Number (ISSN)

  • 0271-4310

International Standard Book Number 13 (ISBN-13)

  • 9781728103976

Digital Object Identifier (DOI)

  • 10.1109/ISCAS.2019.8702756

Citation Source

  • Scopus