Analog neuromorphic system based on multi input floating gate MOS neuron model

Tripathi, A and Arabizadeh, M and Khandelwal, S and Thakur, CS (2019) Analog neuromorphic system based on multi input floating gate MOS neuron model. In: 2019 IEEE International Symposium on Circuits and Systems, ISCAS 2019, 26 - 29 May 2019, Sapporo.

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Abstract

This paper introduces a novel implementation of the low-power analog artificial neural network (ANN) using Multiple Input Floating Gate MOS (MIFGMOS) transistor for machine learning applications. The number of inputs to a neuron in an ANN is the major bottleneck in building a large scale analog system. The proposed MIFGMOS transistor enables to build a large scale system by combining multiple inputs in a single transistor with a small silicon footprint. Here, we show the MIFGMOS based implementation of the Extreme Learning Machine (ELM) architecture using the receptive field approach with transistor operating in the sub-threshold region. The MIFGMOS produces output current as a function of the weighted combination of the voltage applied to its gate terminals. In the ELM architecture, the weights between the input and the hidden layer are random and this allows exploiting the random device mismatch due to the fabrication process, for building Integrated Circuits (IC) based on ELM architecture. Thus, we use implicit random weights present due to device mismatch, and there is no need to store the input weights. We have verified our architecture using circuit simulations on regression and various classification problems such as on the MNIST data-set and a few UCI data-sets. The proposed MIFGMOS enables combining multiple inputs in a single transistor and will thus pave the way to build large scale deep learning neural networks.

Item Type:	Conference Paper
Publication:	Proceedings - IEEE International Symposium on Circuits and Systems
Publisher:	Institute of Electrical and Electronics Engineers Inc.
Additional Information:	The copyright for this article belongs to the Institute of Electrical and Electronics Engineers Inc.
Keywords:	Circuit simulation; Classification (of information); Deep learning; Large scale systems; Learning algorithms; Network architecture; Neural networks; Transistors, Extreme learning machine; Fabrication process; Learning neural networks; Machine learning applications; MIFGMOS; Multiple input floating gates; Neuromorphic systems; Sub-threshold regions, Low power electronics
Department/Centre:	Division of Electrical Sciences > Electronic Systems Engineering (Formerly Centre for Electronic Design & Technology)
Date Deposited:	23 Nov 2022 04:56
Last Modified:	23 Nov 2022 04:56
URI:	https://eprints.iisc.ac.in/id/eprint/77946

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