Design of accurate stochastic number generators with noisy emerging devices for stochastic computing

Abstract

Stochastic computing (SC) is an unconventional computing paradigm that operates on stochastic bit streams. It has gained attention recently because of the very low area and power needs of its computing core. SC relies on stochastic number generators (SNGs) to map input binary numbers to stochastic bit streams. A conventional SNG comprises a random number source (RNS), typically an LFSR, and a comparator. It needs far more area and power than the SC core, offsetting the latter's main advantages. To mitigate this problem, SNGs employing emerging nanoscale devices such as memristors and spintronic devices have been proposed. However, these devices tend to have large errors in their output probabilities due to unpredictable variations in their fabrication processes and noise in their control signals. We present a novel method of exploiting such devices to design a highly accurate SNG. It is built around an RNS that generates uniformly distributed random numbers under ideal (nominal) conditions. It also has a novel error-cancelling probability conversion circuit (ECPCC) that guarantees very high accuracy in the output probability under realistic conditions when the RNS is subject to errors. An ECPCC can also be used to generate maximally correlated stochastic streams, a useful property for some applications.