In-memory flow-based stochastic computing on memristor crossbars using bit-vector stochastic streams

Abstract

Nanoscale memristor crossbars provide a natural fabric for in-memory computing and have recently been shown to efficiently perform exact logical operations by exploiting the flow of current through crossbar interconnects. In this paper, we extend the flow-based crossbar computing approach to approximate stochastic computing. First, we show that the natural flow of current through probabilistically-switching memristive nano-switches in crossbars can be used to perform approximate stochastic computing. Second, we demonstrate that optimizing the approximate stochastic computations in terms of the number of required random bits leads to stochastic computing using bit-vector stochastic streams of varying bit-widths — a hybrid of the traditional full-width bit-vector computing approach and the traditional bit-stream stochastic computing methodology. This hybrid approach based on bit-vector stochastic streams of different bit-widths can be efficiently implemented using an in-memory nanoscale memristive crossbar computing framework.