Exploring Target Function Approximation for Stochastic Circuit Minimization

Stochastic computing (SC) is an emerging paradigm for designing circuits to perform complicated computation with simple circuitry. Although SC circuits have small area and critical-path delay, due to the need of many clock cycles to perform computation, they have a large overall latency and energy consumption. One solution to this problem is to further minimize the circuits. In this work, we explore target function approximation to derive an SC circuit with significantly reduced area and delay. We propose two static methods that first construct a set of functions close to the given target function and then select the best synthesized SC circuit realizing one of these functions. We also propose an efficient dynamic method that simultaneously searches for the best approximated target function and the corresponding minimized SC circuit. The experimental results show that on average, our dynamic method dramatically reduces the area, critical-path delay, and area-delay product of the SC circuits by 80%, 59%, and 91%, respectively, over the state-of-the-art Maclaurin polynomial-based method for a given error bound of 2%. The code of our methods is made open-source.