Mask Technique for Fast and Efficient Training of Binary Resistive Crossbar Arrays

Abstract

Resistive crossbar arrays, despite their advantages such as parallel processing, low power, and high density, suffer from the sneak path problem created by IR voltage drops. The crossbar non-idealities considerably degrade the accuracy of matrix multiplication operation, which is the cornerstone of hardware accelerated neural networks. In this paper, we propose a novel technique to capture the sneak path problem to enable off-chip learning and weight transfer. The proposed technique can be easily integrated into any neural network training framework. Performance results show a significant improvement after retraining the network with the proposed mask technique. Two mask solutions have been proposed and studied to capture the sneak path problem in resistive crossbar arrays. Both mask solutions were successfully able to achieve classification accuracy close to the baseline accuracy. The tradeoffs between the two solutions are discussed and compared in terms of accuracy, power, and area.