TSCIM: A 28nm Transposed Stochastic CIM Macro for On-Chip Training and Inference
Yu Liu, Yang Lou, Kangkang Mao, Xin Li, Chenghu Dai, Xiulong Wu, Zhiting Lin · 2025
This work introduces a novel Transposed Stochastic Computing-in-Memory (TSCIM) macro designed to enhance the efficiency of on-chip training and inference. The macro incorporates a novel stochastic quantization strategy and utilizes a transposed separated wordline SRAM to enable multi-bit signed MAC operations. Furthermore, a stochastic adder tree is utilized to minimize area and power consumption overhead. The design includes a 4Kb SRAM CIM macro implemented in 28 nm CMOS technology. Simulation results show that the power consumption of the stochastic accumulation circuit (SAC) is reduced by 63.6%, while the area overhead is decreased by a factor of 7.73 compared to designs using full adder (FA) adder trees. Additionally, the computation latency is decreased by 16× compared to traditional stochastic circuits. The TSCIM macro can achieve a peak energy efficiency of 63.02 TOPS/W and an area efficiency of 15.54 TOPS/mm2.