Unsupervised Learning in a Ternary SNN Using STDP

This paper proposes a novel implementation of a ternary Spiking Neural Network (SNN) and investigates it using a hierarchical simulation framework. The proposed ternary SNN is trained in an unsupervised manner using the Spike Timing Dependent Plasticity (STDP) learning rule. A ternary neuron is impl...

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Bibliographic Details
Main Authors: Abhinav Gupta, Sneh Saurabh
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Journal of the Electron Devices Society
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Online Access:https://ieeexplore.ieee.org/document/10437991/
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Summary:This paper proposes a novel implementation of a ternary Spiking Neural Network (SNN) and investigates it using a hierarchical simulation framework. The proposed ternary SNN is trained in an unsupervised manner using the Spike Timing Dependent Plasticity (STDP) learning rule. A ternary neuron is implemented using a Dual-Pocket Tunnel Field effect transistor (DP-TFET). The synapse consists of a Magnetic Tunnel Junction (MTJ) with a Heavy Metal (HM) underlayer, allowing for the adjustment of its conductance by directing a current through the HM layer. Further, we show that a pair of dual-pocket Fully-Depleted Silicon-on-Insulator (FD-SOI) MOSFETs can be utilized to generate a current, which reduces exponentially with increasing duration of firing events between pre- and post-synaptic neurons. This current modulates the synapse&#x2019;s conductance according to STDP. Furthermore, it is demonstrated that the proposed ternary SNN can be trained to classify digits in the MNIST dataset with an accuracy of 82&#x0025;, which is better (75&#x0025;) than that obtained using a binary SNN. Moreover, the runtime required to train the proposed ternary SNN is <inline-formula> <tex-math notation="LaTeX">$8\times $ </tex-math></inline-formula> less than that required for a binary SNN.
ISSN:2168-6734