Training neural networks using memristive devices with nonlinear accumulative behavior

Abstract

Memristive devices when organized in crossbar arrays can be used to accelerate the training of deep neural networks through in-memory computing. In this paper, we propose a scheme that addresses a key challenge, namely, the non-linear accumulative behavior of memristive devices. Data entering and leaving the memristive crossbar array is pre- A nd post-processed to change the effective mapping of weight matrices onto the conductance matrix. The impact of the proposed scheme is studied for training neural networks using phase-change memory (PCM) devices as synaptic elements. The scheme is shown to significantly improve the network's classification accuracy, allowing us to reach the performance of an ideal linear device.