Memristor Crossbar Tiles in a Flexible, General Purpose Neural Processor

Memristor crossbar arrays have been proposed for use in synthetic neuron hardware due to their high density, non-volatile programmable conductances, and ability to perform dot-product computation efficiently. However, fitting complete neural networks into multiple, uniformly sized crossbars often results in poor memristor and neuron utilization. In this paper, we propose the use of smaller crossbar tiles that can be flexibly combined to create a variety of crossbar sizes and aspect ratios to more closely fit the ideal sizes required. We examine the throughput/power and throughput/area metrics of custom versus tiled designs for three different neural network applications, representing widely differing design points. We demonstrate that a general purpose neural processor, using a single tile size, compares reasonably with highly specialized neural processor designs using multiple, optimally sized custom crossbars. These results indicate that general purpose memristive crossbar-based neural processors are practical.