Voltage-Driven Domain-Wall Motion based Neuro-Synaptic Devices for Dynamic On-line Learning

TL;DR

This paper introduces voltage-driven domain-wall motion devices for neuromorphic computing, enabling energy-efficient, dynamic online learning by mimicking biological neurons and synapses with novel ferroelectric and ferromagnetic materials.

Contribution

It proposes a new voltage-controlled neuro-synaptic device using domain wall motion, enabling efficient, on-line learning in spiking neural networks.

Findings

Device simulation confirms feasibility of voltage-driven domain wall motion.

Proposed devices enable energy-efficient, dynamic online learning.

Suitable for lifelong learning in neuromorphic systems.

Abstract

Conventional von-Neumann computing models have achieved remarkable feats for the past few decades. However, they fail to deliver the required efficiency for certain basic tasks like image and speech recognition when compared to biological systems. As such, taking cues from biological systems, novel computing paradigms are being explored for efficient hardware implementations of recognition/classification tasks. The basic building blocks of such neuromorphic systems are neurons and synapses. Towards that end, we propose a leaky-integrate-fire (LIF) neuron and a programmable non-volatile synapse using domain wall motion induced by magneto-electric effect. Due to a strong elastic pinning between the ferro-magnetic domain wall (FM-DW) and the underlying ferro-electric domain wall (FE-DW), the FM-DW gets dragged by the FE-DW on application of a voltage pulse. The fact that FE materials are insulators allows for pure voltage-driven FM-DW motion, which in turn can be used to mimic the behaviors of biological spiking neurons and synapses. The voltage driven nature of the proposed devices allows energy-efficient operation. A detailed device to system level simulation framework based on micromagnetic simulations has been developed to analyze the feasibility of the proposed neuro-synaptic devices. We also demonstrate that the energy-efficient voltage-controlled behavior of the proposed devices make them suitable for dynamic on-line and lifelong learning in spiking neural networks (SNNs).