Please use this identifier to cite or link to this item: https://doi.org/10.1109/TBCAS.2018.2867038
Title: Domain Wall Motion-Based Dual-Threshold Activation Unit for Low-Power Classification of Non-Linearly Separable Functions
Authors: Deb, Suman
Vatwani, Tarun
Chattopadhyay, Anupam
Basu, Arindam
Fong, Xuanyao 
Keywords: Science & Technology
Technology
Engineering, Biomedical
Engineering, Electrical & Electronic
Engineering
ANN
domain wall motion
dual-threshold activation unit
learning algorithm
memristive crossbar array
neuromorphic computing
non-linearly separable function
threshold function
DYNAMICS
CIRCUIT
NETWORK
Issue Date: 1-Dec-2018
Publisher: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation: Deb, Suman, Vatwani, Tarun, Chattopadhyay, Anupam, Basu, Arindam, Fong, Xuanyao (2018-12-01). Domain Wall Motion-Based Dual-Threshold Activation Unit for Low-Power Classification of Non-Linearly Separable Functions. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 12 (6) : 1410-1421. ScholarBank@NUS Repository. https://doi.org/10.1109/TBCAS.2018.2867038
Abstract: © 2007-2012 IEEE. Recently, a great deal of scientific endeavour has been devoted to developing spin-based neuromorphic platforms owing to the ultra-low-power benefits offered by spin devices and the inherent correspondence between spintronic phenomena and the desired neuronal, synaptic behavior. While domain wall motion-based threshold activation unit has previously been demonstrated for neuromorphic circuits, it remains well known that neurons with threshold activation cannot completely learn nonlinearly separable functions. This paper addresses this fundamental limitation by proposing a novel domain wall motion-based dual-threshold activation unit with additional nonlinearity in its function. Furthermore, a new learning algorithm is formulated for a neuron with this activation function. We perform 100 trials of tenfold training and testing of our neural networks on real-world datasets taken from the UCI machine learning repository. On an average, the proposed algorithm achieves 1.04 ×-6.54× lower misclassification rate (MCR) than the traditional perceptron learning algorithm. In a circuit-level simulation, the neural networks with the proposed activation unit are observed to outperform the perceptron networks by as much as 2.98 × MCR. The energy consumption of a neuron having the proposed domain wall motion-based activation unit averages to 35 fJ approximately.
Source Title: IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS
URI: https://scholarbank.nus.edu.sg/handle/10635/156170
ISSN: 19324545
19409990
DOI: 10.1109/TBCAS.2018.2867038
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