DOI
https://doi.org/10.25772/AKNK-B502
Defense Date
2023
Document Type
Thesis
Degree Name
Doctor of Philosophy
Department
Computer Science
First Advisor
Krzysztof J. Cios
Abstract
This dissertation proposes ways to address current limitations of neuromorphic computing to create energy-efficient and adaptable systems for AI applications. It does so by designing novel spiking neural networks architectures that improve their performance. Specifically, the two proposed architectures address the issues of training complexity, hyperparameter selection, computational flexibility, and scarcity of neuromorphic training data. The first architecture uses auxiliary learning to improve training performance and data usage, while the second architecture leverages neuromodulation capability of spiking neurons to improve multitasking classification performance. The proposed architectures are tested on Intel's Loihi2 neuromorphic chip using several neuromorphic datasets, such as NMIST, DVSCIFAR10, and DVS128-Gesture. The presented results demonstrate potential of the proposed architectures but also reveal some of their limitations which are proposed as future research.
Rights
© The Author
Is Part Of
VCU University Archives
Is Part Of
VCU Theses and Dissertations
Date of Submission
8-10-2023