Application of stochastic computing in brainware

Abstract

This paper reviews applications of stochastic computing in brainware LSI (BLSI) for visual information processing. Stochastic computing exploits random bit streams, realizing the area-efficient hardware of complicated functions, such as multiplication and tanh functions in comparison with binary computation. Using stochastic computing, we implement the hardware of several physiological models of the primary visual cortex of brains, where these models require such the complicated functions. Our vision BLSIs are implemented using Taiwan Semiconductor Manufacturing Company (TSMC) 65 nm CMOS process and discussed with traditional fixed-point implementations in terms of hardware performance and computation accuracy. In addition, an analog-to-stochastic converter is designed using CMOS and magnetic tunnel junctions that exhibit probabilistic switching behaviors for area/energy-efficient signal conversions to stochastic bit streams.