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

Deliang Fan, Yong Shim, Anand Raghunathan, Kaushik Roy

Research output: Contribution to journalArticlepeer-review

35 Scopus citations

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 around two orders of magnitude lower energy consumption.

Original languageEnglish (US)
Article number7113894
Pages (from-to)1013-1023
Number of pages11
JournalIEEE Transactions on Nanotechnology
Volume14
Issue number6
DOIs
StatePublished - Nov 2015
Externally publishedYes

Keywords

  • Artificial neural networks
  • Magnetic domain walls
  • Magnetic domains
  • Magnetic tunneling
  • Neurons
  • Resistance
  • Transfer functions

ASJC Scopus subject areas

  • Computer Science Applications
  • Electrical and Electronic Engineering

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