A Reconfigurable 1T1C eDRAM-based Spiking Neural Network Computing-In-Memory Processor for High System-Level Efficiency

Seryeong Kim, Soyeon Kim, Soyeon Um, Sangjin Kim, Zhiyong Li, Sangyeob Kim, Wooyoung Jo, Hoi-Jun Yoo

Abstract: Spiking Neural Network (SNN) Computing-In-Memory (CIM) was proposed for high macro-level energy efficiency. However, system-level energy efficiency is limited by EMA due to a large intermediate activation footprint requirement. To reduce the EMA, a large capacity SNN CIM is needed to load tons of weights in the CIM. This paper proposes a high-density 1T1C eDRAM-based SNN CIM processor for supporting high system-level energy efficiency with two key features: 1) High-density and low-power Reconfigurable Neuro-Cell Array (ReNCA) for memory and SNN peripheral logic using a charge pump and reusing 1T1C cell array, achieving 41% area and 90% power reduction compared to previous work. 2) Reconfigurable CIM architecture with dual-mode ReNCA and Dynamic Adjustable Neuron Link (DAN Link) for layer fusion increases system-level efficiency including intermediate and weight EMA. It achieves <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$10\times$</tex> higher state-of-the-art system-level energy efficiency including EMA.