SCGen: A Versatile Generator Framework for Agile Design of Stochastic Circuits

Zexi Li, Haoran Jin, Kuncai Zhong, Guojie Luo, Runsheng Wang, Weikang Qian

Abstract: Stochastic computing (SC) is an unconventional computing paradigm with unique features. Designing SC circuits is dramatically different from designing binary computing (BC) circuits. To support the agile design of SC circuits, we propose SCGen, a versatile generator framework, which provides users with a C++ interface to easily specify SC circuits and supports 1) accelerated accuracy simulation, 2) accelerated design space exploration (DSE) for accuracy maximization guided by simulated annealing (SA) and genetic algorithm (GA), 3) circuit optimization by random number source (RNS) sharing, 4) circuit verification via symbolic expression analysis, and 5) automatic Verilog code generation. Furthermore, we extend SCGen to also support agile design of hybrid SC-BC circuits. The experimental results show that our proposed DSE acceleration methods achieve up to 59x speedup, the DSE with SA and GA can get an average reduction of 4.0% and 12.7%, respectively, in accuracy loss compared to random search, and RNS sharing reduces the average area and power by 41% and 47%, respectively.