A Mixed Signal 65nm CMOS Implementation of a Spiking Neural Network

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Date

2022-08-26

Authors

Yan, Yangtian

Advisor

Wright, Derek

Journal Title

Journal ISSN

Volume Title

Publisher

University of Waterloo

Abstract

Spiking neural networks (SNNs) are an emerging class of biologically inspired Artificial Neural Networks implemented in machine learning and artificial intelligence. Current state-of-the-art small- and large-scale SNNs are mainly implemented as digital hardware with time-multiplexing techniques to achieve power efficiency. In this thesis, a 65 nm CMOS mixed signal asynchronous SNN implementation was designed and simulated. The proposed design reduces hardware and timing complexity over existing implementations and opens opportunities for further larger-scale implementations.

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Keywords

artificial intelligence, CMOS, neural network, artificial neural network, spiking neural network, machine learning, mixed signal IC

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