Area-efficient and Low-power Face-to-Face-bonded 3D Liquid State Machine Design

Abstract

As small-form-factor and low-power end devices matter in the cloud networking and Internet-of-Things Era, the bio-inspired neuromorphic architectures attract great attention recently in the hope of reaching the energy-efficiency of brain functions. Out of promising solutions, a liquid state machine (LSM), that consists of randomly and recurrently connected reservoir neurons and trainable readout neurons, has shown a great promise in delivering brain-inspired computing power. In this work, we adopt the state-of-the-art face-to-face (F2F)-bonded 3D IC flow named Compact-2D [4] to the LSM processor design, and study the power-area-accuracy benefits of 3D LSM ICs targeting the next generation commercial-grade neuromorphic computing platforms. First, we analyze how the different size and connection density of a reservoir in the LSM architecture affects the learning performance using the real-world speech recognition benchmark. Also, we explore how much the power-area design overhead should be paid off to enable better classification accuracy. Based on the power-area-accuracy trade-off, we implement a F2F-bonded 3D LSM IC using the optimal LSM architecture, and finally justify that 3D integration practically benefits the LSM processor design in huge form factor and power savings while preserving the best learning performance.