PAWN: Programmed Analog Weights for Non-Linearity Optimization in Memristor-Based Neuromorphic Computing System

Abstract

Memristors offer advantages as a hardware solution for neuromorphic systems. However, their nonlinear device property makes the weight update inaccurately and reduces the inference accuracy of a neural network. A Programmed Analog Weights for Nonlinearity (PAWN) method is proposed in this paper to update the conductance of a memristor by following the nonlinear curve during the training in a neuromorphic system. The experiment results indicates the PAWN method is effective to alleviate the nonlinearity influence to memristors in all different LTP/LTD conditions. Especially in extreme nonlinearity (LTP=6, LTD=−6), the memristor-based neuromorphic system has significantly low accuracy (51.77%), but the PAWN method enables large accuracy improvement (9.87%) without the inference energy and latency overhead. In addition, overall performance of the neuromorphic system is also evaluated for further verification. Finally, comprehensive experiments show that the PAWN method is still greatly valid even considering device-to-device variations, cycle-to-cycle variations, various technology nodes, and different architectures.