Resilience and Robustness of Spiking Neural Networks for Neuromorphic Systems

Conference · Wed Jul 01 04:00:00 EDT 2020

DOI:https://doi.org/10.1109/IJCNN48605.2020.9207560· OSTI ID:1671419

Schuman, Catherine ^[1]; Mitchell, Parker ^[1]; Johnston, Travis ^[1]; Parsa, Maryam ^[2]; Kay, Bill ^[1]; Date, Prasanna ^[1]; Patton, Robert ^[1]

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1. ORNL
2. Purdue University

Though robustness and resilience are commonly quoted as features of neuromorphic computing systems, the expected performance of neuromorphic systems in the face of hardware failures is not clear. In this work, we study the effect of failures on the performance of four different training algo-rithms for spiking neural networks on neuromorphic systems: two back-propagation-based training approaches (Whetstone and SLAYER), a liquid state machine or reservoir computing approach, and an evolutionary optimization-based approach (EONS). We show that these four different approaches have very different resilience characteristics with respect to simulated hardware failures. We then analyze an approach for training more resilient spiking neural networks using the evolutionary optimization approach. We show how this approach produces more resilient networks and discuss how it can be extended to other spiking neural network training approaches as well.

• Research Organization: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
• Sponsoring Organization: USDOE; USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR) (SC-21)
• DOE Contract Number: AC05-00OR22725
• OSTI ID: 1671419
• Country of Publication: United States
• Language: English