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Memristive Device Variability Performance Impact on Neuromorphic Machine Learning Hardware | IEEE Conference Publication | IEEE Xplore

Memristive Device Variability Performance Impact on Neuromorphic Machine Learning Hardware


Abstract:

A vital issue regarding hardware implementations of machine learning algorithms with novel memristive devices is the concern of the proposed architecture's resilience to ...Show More

Abstract:

A vital issue regarding hardware implementations of machine learning algorithms with novel memristive devices is the concern of the proposed architecture's resilience to high device variability. We find that most algorithms have surprisingly high tolerance to variable weight updates and initializations. We also propose a simple method to validate Single Layer Perceptron (SLP) neuromorphic hardware based on memristive RRAM crossbar arrays by studying accuracy vs. training time. Finally, we show high level simulations of an RRAM cell with intermediate states, decay, and Gaussian variability.
Date of Conference: 19-22 October 2020
Date Added to IEEE Xplore: 28 December 2020
ISBN Information:
Conference Location: Pullman, WA, USA

Funding Agency:


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