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arXiv · 2009.00180

AIDX: Adaptive Inference Scheme to Mitigate State-Drift in Memristive VMM Accelerators

Tony Liu·Amirali Amirsoleimani·Fabien Alibart·Serge Ecoffey·Dominique Drouin·Roman Genov
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Abstract

An adaptive inference method for crossbar (AIDX) is presented based on an optimization scheme for adjusting the duration and amplitude of input voltage pulses. AIDX minimizes the long-term effects of memristance drift on artificial neural network accuracy. The sub-threshold behavior of memristor has been modeled and verified by comparing with fabricated device data. The proposed method has been evaluated by testing on different network structures and applications, e.g., image reconstruction and classification tasks. The results showed an average of 60% improvement in convolutional neural network (CNN) performance on CIFAR10 dataset after 10000 inference operations as well as 78.6% error reduction in image reconstruction.

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Tony Liu, Amirali Amirsoleimani, Fabien Alibart, Serge Ecoffey, Dominique Drouin, Roman Genov. 2020-09-01. AIDX: Adaptive Inference Scheme to Mitigate State-Drift in Memristive VMM Accelerators. https://arxiv.org/abs/2009.00180

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