A 3-D Reconfigurable RRAM Crossbar Inference Engine

Abstract

Deep neural network inference accelerators are rapidly growing in importance as we turn to massively parallelized processing beyond GPUs and ASICs. The dominant operation in feedforward inference is the multiply-and-accumlate process, where each column in a crossbar generates the current response of a single neuron. As a result, memristor crossbar arrays parallelize inference and image processing tasks very efficiently. In this brief, we present a 3-D active memristor crossbar array 'CrossStack', which adopts stacked pairs of Al/TiO 2/TiO2 -x/Al devices with common middle electrodes. By designing CMOS-memristor hybrid cells used in the layout of the array, CrossStack can operate in one of two user-configurable modes as a reconfigurable inference engine: 1) expansion mode and 2) deep-net mode. In expansion mode, the resolution of the network is doubled by increasing the number of inputs for a given chip area, reducing IR drop by 22%. In deep-net mode, inference speed per-10-bit convolution is improved by 29% by simultaneously using one TiO 2/TiO2 -x layer for read processes, and the other for write processes. We experimentally verify both modes on our 10 × 10 × 2 array.Index Terms-deep learning, in-memory computing, memristors, neural network, RRAM.