LUNA-CiM: A Programmable Compute-in-Memory Fabric for Neural Network Acceleration

Abstract

Compute-in-memory (CiM) has emerged as a promising approach for improving energy efficiency for diverse data-intensive applications. In this paper, we present LUNA-CiM, a lookup table (LUT)-based programmable fabric for flexible and efficient mapping of artificial neural network (ANN) in memory. Its objective is to tackle scalability challenges in LUT-based computation by minimizing hardware, storage elements, and energy consumption. The proposed method utilizes the divide and conquer (D&C) strategy to enhance the scalability of LUT-based computation. For example, in a 4b $\boldsymbol{\times}$ 4b lookup table-based multiplier, as one of the main components in ANN, decomposing high-precision operations into lower-precision counterparts leads to a substantial reduction in area overheads, approximately 73% less compared to conventional LUT-based approaches. Importantly, this efficiency gain is achieved without compromising accuracy. Extensive simulations were conducted to validate the performance of the proposed method. The analysis presented in this paper reveals a noteworthy advancement in energy efficiency, indicating a 58% reduction in energy consumption per computation compared to the conventional lookup table approach. Additionally, the introduced approach demonstrates a 36% improvement in speed over the traditional lookup table approach. These findings highlight notable advancements in performance, showcasing the potential of this inventive method to achieve low power, low-area overhead, and fast computations through the utilization of LUTs within an SRAM array.