An SRAM-based Stochastic Number Generator for Stochastic Computing
Heng Shi, Zhengkun Yu, Tingting Zhang, Jie Han, Yumeng Yang, Siting Liu · 2025
Stochastic computing (SC) features a unique number representation, where real values are encoded by the probability of "1"s in a random binary bit stream or a stochastic sequence. It enables hardware-efficient arithmetic circuit designs with simple logic gates. However, stochastic number generators (SNGs) are required to produce stochastic sequences. The high hardware cost of an SNG offsets the advantage of SC. To reduce the hardware cost of an SNG, we propose an SRAM-based SNG using voltage under-scaling. It generates random bits by leveraging the access instability of selected SRAM cells, induced by a reduced supply voltage. It is suitable for energy-efficient SC. We implemented the SRAM-based SNG on a Xilinx ZC702 FPGA using block RAMs and evaluated its performance across multiple SC applications, including finite-state machine-based tanh function generation and an Ising machine that solves max-cut problems (MCPs). For the tanh function, our design achieves a comparable mean-squared error (MSE) (8.7 × 10−3) compared to the use of traditional SNGs, such as Sobol- (4.67 × 10−2) and linear feedback shift register (LFSR)-based (1.53 × 10−2) ones. For MCPs, a maximum cut value comparable to that of a cutting-edge design is achieved. Compared with LFSR- and Sobol-based designs, the proposed design consumes 84.3% and 92.5% less energy, respectively.