Date of Award
2022
Publication Type
Dissertation
Degree Name
Ph.D.
Department
Electrical and Computer Engineering
Keywords
Digital hardware, Spiking neural networks, Spiking neurons, Pattern recognition applications
Supervisor
M. Mirhassani
Supervisor
R. Muscedere
Rights
info:eu-repo/semantics/openAccess
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Abstract
Artificial spiking neural networks are gaining increasing prominence due to their potential advantages over traditional, time-static artificial neural networks. Custom hardware implementations of spiking neural networks present many advantages over other implementation mediums. Two main topics are the focus of this work. Firstly, digital hardware implementations of spiking neurons and neuromorphic hardware are explored and presented. These implementations include novel implementations for lowered digital hardware requirements and reduced power consumption.
The second section of this work proposes a novel method for selectively adding sparsity to a spiking neural network based on training set images for pattern recognition applications, thereby greatly reducing the inference time required in a digital hardware implementation.
Recommended Citation
Leigh, Alexander J., "Hardware Implementations of Spiking Neural Networks and Artificially Intelligent Systems" (2022). Electronic Theses and Dissertations. 8907.
https://scholar.uwindsor.ca/etd/8907