Date of Graduation
5-2021
Document Type
Thesis
Degree Name
Master of Science in Electrical Engineering (MSEE)
Degree Level
Graduate
Department
Electrical Engineering
Advisor/Mentor
Jeff Dix
Committee Member
Alan Mantooth
Second Committee Member
Zhong Chen
Keywords
Analog Integrated Circuits, Machine Learning, Neural Networks, Spiking Neural Networks
Abstract
Machine learning is a rapidly accelerating tool and technology used for countless applications in the modern world. There are many digital algorithms to deploy a machine learning program, but the most advanced and well-known algorithm is the artificial neural network (ANN). While ANNs demonstrate impressive reinforcement learning behaviors, they require large power consumption to operate. Therefore, an analog spiking neural network (SNN) implementing spike timing-dependent plasticity is proposed, developed, and tested to demonstrate equivalent learning abilities with fractional power consumption compared to its digital adversary.
Citation
Vincent, L. (2021). Analog Spiking Neural Network Implementing Spike Timing-Dependent Plasticity on 65 nm CMOS. Graduate Theses and Dissertations Retrieved from https://scholarworks.uark.edu/etd/4048
Included in
Artificial Intelligence and Robotics Commons, Electrical and Electronics Commons, Graphics and Human Computer Interfaces Commons, Power and Energy Commons