Design and Architectural Co-optimization of Monolithic 3D Liquid State Machine-based Neuromorphic Processor

Abstract

A liquid state machine (LSM) is a powerful recurrent spiking neural network shown to be effective in various learning tasks including speech recognition. In this work, we investigate design and architectural co-optimization to further improve the area-energy efficiency of LSM-based speech recognition processors with monolithic 3D IC (M3D) technology. We conduct fine-grained tier partitioning, where individual neurons are folded, and explore the impact of shared memory architecture and synaptic model complexity on the power-performance-area-accuracy (PPA) benefit of M3D LSM-based speech recognition. In training and classification tasks using spoken English letters, we obtain up to 70.0% PPAA savings over 2D ICs.