ReCIM: A SRAM‐Based Digital–Analogue Hybrid CIM Reformer Accelerator Macro

Reformer reduces redundant self‐attention computations via hash bucketing. In this study, we introduce a SRAM‐based digital‐analogue hybrid reformer computing‐in‐memory (ReCIM) accelerator macro. This macro presents an absolute maximum value addressing circuit which facilitates the hash bucketing process and enables the utilisation of strongly‐correlated (S‐C) vectors for attention mechanism computations, thereby improving computational efficiency and saving memory space. Additionally, we introduce a reusable weight array which is suitable for matrix operations across various processes of self‐attention, minimising unnecessary area overhead and enhancing device reusability. The proposed 4 Kb ReCIM macro was analysed using 28‐nm CMOS technology. Simulation results demonstrate that the macro achieves a frequency of 500 MHz at a supply voltage of 0.9 V. During the hash bucketing process, energy efficiency reaches 9.74 TOPS/W.