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Title: Spin switches for compact implementation of neuron and synapse

Nanomagnets driven by spin currents provide a natural implementation for a neuron and a synapse: currents allow convenient summation of multiple inputs, while the magnet provides the threshold function. The objective of this paper is to explore the possibility of a hardware neural network implementation using a spin switch (SS) as its basic building block. SS is a recently proposed device based on established technology with a transistor-like gain and input-output isolation. This allows neural networks to be constructed with purely passive interconnections without intervening clocks or amplifiers. The weights for the neural network are conveniently adjusted through analog voltages that can be stored in a non-volatile manner in an underlying CMOS layer using a floating gate low dropout voltage regulator. The operation of a multi-layer SS neural network designed for character recognition is demonstrated using a standard simulation model based on coupled Landau-Lifshitz-Gilbert equations, one for each magnet in the network.
Authors:
; ;  [1] ;  [2]
  1. School of Electrical and Computer Engineering, Purdue University, West Lafayette, Indiana 47907 (United States)
  2. GLOBALFOUNDRIES, Inc., Sunnyvale, California 94085 (United States)
Publication Date:
OSTI Identifier:
22300082
Resource Type:
Journal Article
Resource Relation:
Journal Name: Applied Physics Letters; Journal Volume: 104; Journal Issue: 22; Other Information: (c) 2014 AIP Publishing LLC; Country of input: International Atomic Energy Agency (IAEA)
Country of Publication:
United States
Language:
English
Subject:
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS; AMPLIFIERS; CURRENTS; EQUATIONS; LAYERS; MAGNETS; MATERIAL BALANCE; NERVE CELLS; NEURAL NETWORKS; SIMULATION; SPIN; SWITCHES; TRANSISTORS; VOLATILITY; VOLTAGE REGULATORS