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Lookup NU author(s): Dr Sergey Mileiko, Thomas Bunnam, Dr Fei Xia, Dr Rishad Shafik, Professor Alex Yakovlev, Professor Shidhartha Das
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
Neural networks (NNs) are steering a new generation of artificial intelligence (AI) applications at the micro-edge. Examples include wireless sensors, wearables and cybernetic systems that collect data and process them to support real-world decisions and controls. For energy autonomy, these applications are typically powered by energy harvesters. As harvesters and other power sources which provide energy autonomy inevitably have power variations, the circuits need to robustly operate over a dynamic power envelope. In other words, the NN hardware needs to be able to function correctly under unpredictable and variable supply voltages. In this paper, we propose a novel NN design approach using the principle of pulse width modulation (PWM). PWM signals represent information with their duty cycle values which may be made independent of the voltages and frequencies of the carrier signals. We design a PWM-based perceptron which can serve as the fundamental building block for NNs, by using an entirely new method of realizing arithmetic in the PWM domain. We analyse the proposed approach building from a 3 × 3 perceptron circuit to a complex multi-layer NN. Using handwritten character recognition as an exemplar of AI applications, we demonstrate the power elasticity, resilience and efficiency of the proposed NN design in the presence of functional and parametric variations including large voltage variations in the power supply. This article is part of the theme issue 'Harmonizing energy-autonomous computing and intelligence'.
Author(s): Mileiko S, Bunnam T, Xia F, Shafik R, Yakovlev A, Das S
Publication type: Article
Publication status: Published
Journal: Philosophical Transactions of the Royal Society A
Year: 2020
Volume: 378
Issue: 2164
Print publication date: 01/02/2020
Online publication date: 23/12/2019
Acceptance date: 16/10/2019
Date deposited: 15/01/2020
ISSN (print): 1364-503X
ISSN (electronic): 1471-2962
Publisher: The Royal Society Publishing
URL: https://doi.org/10.1098/rsta.2019.0166
DOI: 10.1098/rsta.2019.0166
PubMed id: 31865878
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