Analog Spiking Neural Network Implementing Spike Timing-Dependent Plasticity on 65 nm CMOS

<p>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.</p>

Name thesis:degree_name

Master of Science in Electrical Engineering (MSEE)

Level thesis:degree_level

Thesis

Year dc:date.available

2021

Author dc:creator

• Vincent, Luke

Advisor dc:contributor.advisor

• Dix, Jeff

Contributors dc:contributor

• Mantooth, H. Alan
• Chen, Zhong

• Analog Integrated Circuits
• Machine Learning
• Neural Networks
• Spiking Neural Networks
• Artificial Intelligence and Robotics
• Electrical and Electronics
• Graphics and Human Computer Interfaces
• Power and Energy

Repository record dc:identifier

https://scholarworks.uark.edu/etd/4048

OAI identifier oai:identifier

oai:scholarworks.uark.edu:etd-5598