Look-up table based energy efficient processing in cache support for neural network acceleration

This paper presents a Look-Up Table (LUT) based Processing-In-Memory (PIM) technique with the potential for running Neural Network inference tasks. We implement a bitline computing free technique to avoid frequent bitline accesses to the cache sub-arrays and thereby considerably reducing the memory access energy overhead. LUT in conjunction with the compute engines enables sub-array level parallelism while executing complex operations through data lookup which otherwise requires multiple cycles. Sub-array level parallelism and systolic input data flow ensure data movement to be confined to the SRAM slice.Our proposed LUT based PIM methodology exploits substantial parallelism using look-up tables, which does not alter the memory structure/organization, that is, preserving the bit-cell and peripherals of the existing SRAM monolithic arrays. Our solution achieves 1.72x higher performance and 3.14x lower energy as compared to a state-of-the-art processing-in-cache solution. Sub-array level design modifications to incorporate LUT along with the compute engines will increase the overall cache area by 5.6%. We achieve 3.97x speedup w.r.t neural network systolic accelerator with a similar area. The re-configurable nature of the compute engines enables various neural network operations and thereby supporting sequential networks (RNNs) and transformer models. Our quantitative analysis demonstrates 101x, 3x faster execution and 91x, 11x energy efficient than CPU and GPU respectively while running the transformer model, BERT-Base.