Title:Compact and Accurate Digital Filters Based on Stochastic Computing

Authors: Hideyuki Ichihara, Tatsuyoshi Sugino, Shota Ishii, Tsuyoshi Iwagaki, Tomoo Inoue

Jounrnals:Trans. on Emerging Topics in Comp.

Published Month: 9

Published Year: 2016

Type: article

Stochastic computing (SC), which is an approximate computation with probabilities, has attracted attention as an alternative
to deterministic computing. In this paper, we discuss a design method for compact and accurate digital filters based on SC. Such filter
designs are widely used for various purposes, such as image and signal processing and machine learning. Our design method involves
two techniques. One is sharing random number sources with several stochastic number generators to reduce the areas required by
these generators. Clarifying the influence of the correlation around multiplexers (MUXs) on computation accuracy and utilizing circular
shifts of the output of random number sources, we can reduce the number of random number sources for a digital filter without losing
accuracy. The other technique is to construct a MUX tree, which is the principal part of an SC-based filter. We formulate the
correlation-induced errors produced by the MUX tree, and then propose an algorithm for constructing an optimum MUX tree to
minimize the error. Experimental results show that the proposed design method can derive compact (approximately 70% area
reduction) SC-based filters that retain high accuracy.