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