Circuit Optimization Techniques for Efficient Ex-Situ Training of Robust Memristor Based Liquid State Machine

Author(s)

Alex Henderson, Southwest Research InstituteFollow
Christopher Yakopcic, University of DaytonFollow
Cory Merkel, Rochester Institute of TechnologyFollow
Steven Harbour, Southwest Research InstituteFollow
Tarek M. Taha, University of DaytonFollow
Hananel Hazan, Tufts UniversityFollow

Document Type

Article

Publication Date

2022

Publication Source

Proceedings of the 17th ACM International Symposium on Nanoscale Architectures, Nanoarch 2022

Abstract

Spiking neural network hardware offers a high performance, power-efficient and robust platform for the processing of complex data. Many of these systems require supervised learning, which poses a challenge when using gradient-based algorithms due to the discontinuous properties of SNNs. Memristor based hardware can offer gains in portability, power reduction, and throughput efficiency when compared to pure CMOS. This paper proposes a memristor-based spiking liquid state machine (LSM). The inherent dynamics of the LSM permit the use of supervised learning without backpropagation for weight updates. To carry out the design space evaluation of the LSM for optimal hardware performance, several temporal signal classification tasks are performed. It is found that the binary neuron activations in the output layer improve testing accuracy by 3.7% and 5% for classification, while reducing training time. A power and energy analysis of the proposed hardware is presented, resulting in an approximately 50% reduction in power consumption and cycle energy.

ISBN/ISSN

978-1-4503-9938-8

Document Version

Published Version

Comments

This open-access article is provided for download in compliance with the publisher’s policy on self-archiving. To view the version of record, use the DOI: https://doi.org/10.1145/3565478.3572542

Publisher

ACM

Peer Reviewed

yes

Sponsoring Agency

Assoc Comp Machinery

eCommons Citation

Henderson, Alex; Yakopcic, Christopher; Merkel, Cory; Harbour, Steven; Taha, Tarek M.; and Hazan, Hananel, "Circuit Optimization Techniques for Efficient Ex-Situ Training of Robust Memristor Based Liquid State Machine" (2022). Electrical and Computer Engineering Faculty Publications. 475.
https://ecommons.udayton.edu/ece_fac_pub/475