AdaP-CIM: Compute-in-Memory Based Neural Network Accelerator Using Adaptive Posit
Jingyu He, Fengbin Tu, Kwang-Ting Tim Cheng, Chi-Ying Tsui · 2024
This study proposes two novel approaches to address memory wall issues in AI accelerator designs for large neural networks. The first approach introduces a new format called adaptive Posit (AdaP) with two exponent encoding schemes that dynamically extend the dynamic range of its representation at run time with minimal hardware overhead. The second approach proposes using compute-in-memory (CIM) with speculative input alignment (SAU) to implement the AdaP multiply-and-accumulate (MAC) computation, significantly reducing the delay, area, and power consumption for the max exponent computation. The proposed approaches outperform state-of-the-art quantization methods and achieve significant energy and area efficiency improvements.