Date of Award
Electrical and Computer Engineering
Digital hardware, Spiking neural networks, Spiking neurons, Pattern recognition applications
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
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.
Leigh, Alexander J., "Hardware Implementations of Spiking Neural Networks and Artificially Intelligent Systems" (2022). Electronic Theses and Dissertations. 8907.