Circuit Optimization Techniques for Efficient Ex-Situ Training of Robust Memristor Based Liquid State Machine-Reference-Cited by-同舟云学术

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

Published:2022-12-07 Issue: Volume: Page:
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Container-title:Proceedings of the 17th ACM International Symposium on Nanoscale Architectures
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Author:

Henderson Alex¹^ORCID,Yakopcic Chris²^ORCID,Harbour Steven¹^ORCID,Taha Tarek²^ORCID,Merkel Cory³^ORCID,Hazan Hananel⁴^ORCID

Affiliation:

1. Dayton Engineering Advanced Projects Lab, Southwest Research Institute, Beavercreek, OH, USA

2. Department of Electrical and Computer Engineering, University of Dayton, Dayton, OH, USA

3. Department of Computer Engineering, Rochester Institute of Technology, Rochester, NY, USA

4. Allen Discovery Center, Tufts University, Medford, MA, USA

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3565478.3572542

Reference19 articles.

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2. Reservoir computing approaches to recurrent neural network training

3. B. Schrauwen , D. Verstraeten , and J. Van Campenhout , "An overview of reservoir computing : theory, applications and implementations," in Proceedings of the 15th European Symposium on Artificial Neural Networks , pp. 471 -- 482 , April 2007 B. Schrauwen, D. Verstraeten, and J. Van Campenhout, "An overview of reservoir computing: theory, applications and implementations," in Proceedings of the 15th European Symposium on Artificial Neural Networks, pp. 471--482, April 2007

4. Memristor-The missing circuit element

5. High-Density Crossbar Arrays Based on a Si Memristive System

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