STT-SNN: A Spin-Transfer-Torque Based Soft-Limiting Non-Linear Neuron for Low-Power Artificial Neural Networks

TL;DR

This paper introduces a spin-transfer-torque based soft-limiting neuron that operates at ultra-low power, improving neural network efficiency and accuracy by enabling soft transfer functions in hardware implementations.

Contribution

The paper presents a novel STT device that implements a soft-limiting non-linear neuron, enhancing energy efficiency and modeling capacity in hardware neural networks.

Findings

Achieves over 100x lower energy consumption compared to CMOS neurons.

Demonstrates effective character recognition with the proposed neuron.

Enables ultra-low voltage operation of neural network hardware.

Abstract

Recent years have witnessed growing interest in the use of Artificial Neural Networks (ANNs) for vision, classification, and inference problems. An artificial neuron sums N weighted inputs and passes the result through a non-linear transfer function. Large-scale ANNs impose very high computing requirements for training and classification, leading to great interest in the use of post-CMOS devices to realize them in an energy efficient manner. In this paper, we propose a spin-transfer-torque (STT) device based on Domain Wall Motion (DWM) magnetic strip that can efficiently implement a Soft-limiting Non-linear Neuron (SNN) operating at ultra-low supply voltage and current. In contrast to previous spin-based neurons that can only realize hard-limiting transfer functions, the proposed STT-SNN displays a continuous resistance change with varying input current, and can therefore be employed to implement a soft-limiting neuron transfer function. Soft-limiting neurons are greatly preferred to hard-limiting ones due to their much improved modeling capacity, which leads to higher network accuracy and lower network complexity. We also present an ANN hardware design employing the proposed STT-SNNs and Memristor Crossbar Arrays (MCA) as synapses. The ultra-low voltage operation of the magneto metallic STT-SNN enables the programmable MCA-synapses, computing analog-domain weighted summation of input voltages, to also operate at ultra-low voltage. We modeled the STT-SNN using micro-magnetic simulation and evaluated them using an ANN for character recognition. Comparisons with analog and digital CMOS neurons show that STT-SNNs can achieve more than two orders of magnitude lower energy consumption.