Fast and accurate computation using stochastic circuits

Abstract

Stochastic computing (SC) is a low-cost design technique that has great promise in applications such as image processing. SC enables arithmetic operations to be performed on stochastic bit-streams using ultra-small and low-power circuitry. However, accurate computations tend to require long run-times due to the random fluctuations inherent in stochastic numbers (SNs). We present novel techniques for SN generation that lead to better accuracy/run-time trade-offs. First, we analyze a property called progressive precision (PP) which allows computational accuracy to grow systematically with run-time. Second, borrowing from Monte Carlo methods, we show that SC performance can be greatly improved by replacing the usual pseudo-random number sources by low-discrepancy (LD) sequences that are predictably progressive. Finally, we evaluate the use of LD stochastic numbers in SC, and show they can produce significantly faster and more accurate results than existing stochastic designs.