Towards Theoretical Cost Limit of Stochastic Number Generators for Stochastic Computing

Abstract

Stochastic number generator (SNG) is one important component of stochastic computing (SC). An SNG usually consists of a random number source (RNS) and a probability conversion circuit (PCC). The SNGs occupy a large portion of the total area and power of a stochastic circuit. Thus, it is critical to lower the area and power of the SNGs. The existing methods only focused on simplifying the RNSs inside the SNGs, such as sharing the RNSs and using emerging devices. However, how to reduce the area and power of PCCs is never studied. In this work, we explore this problem and propose a solution that can effectively reduce the area and power of PCCs. We also study the theoretical limit on the cost of SNG and find that our proposed design approaches the limit. The experimental results show that our design can gain up to 2× improvement in power-delay product over the traditional SNGs.