Encoding Neural and Synaptic Functionalities in Electron Spin: A Pathway to Efficient Neuromorphic Computing

TL;DR

This paper reviews recent advances in spintronic devices for neuromorphic computing, highlighting their potential for energy-efficient brain-inspired systems with on-chip learning capabilities.

Contribution

It provides a comprehensive overview of spintronic mechanisms for neural and synaptic functionalities and proposes a co-simulation framework for designing energy-efficient neuromorphic processors.

Findings

Spin-transfer torque mechanisms enable neural and synaptic functionalities.

All-Spin neuromorphic systems could achieve nearly 100x energy efficiency over CMOS.

Device-circuit-algorithm co-simulation aligns with experimental results.

Abstract

Present day computers expend orders of magnitude more computational resources to perform various cognitive and perception related tasks that humans routinely perform everyday. This has recently resulted in a seismic shift in the field of computation where research efforts are being directed to develop a neurocomputer that attempts to mimic the human brain by nanoelectronic components and thereby harness its efficiency in recognition problems. Bridging the gap between neuroscience and nanoelectronics, this paper attempts to provide a review of the recent developments in the field of spintronic device based neuromorphic computing. Description of various spin-transfer torque mechanisms that can be potentially utilized for realizing device structures mimicking neural and synaptic functionalities is provided. A cross-layer perspective extending from the device to the circuit and system level is presented to envision the design of an All-Spin neuromorphic processor enabled with on-chip learning functionalities. Device-circuit-algorithm co-simulation framework calibrated to experimental results suggest that such All-Spin neuromorphic systems can potentially achieve almost two orders of magnitude energy improvement in comparison to state-of-the-art CMOS implementations.