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.
","abstract_html":"<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>","abstract_has_math":false,"creators":["Vincent, Luke"],"institution":null,"degree_name":"Master of Science in Electrical Engineering (MSEE)","degree_level":"Thesis","degree_discipline":null,"degree_department":null,"school":null,"contributors":["Mantooth, H. Alan","Chen, Zhong"],"advisors":["Dix, Jeff"],"committee_chairs":[],"committee_members":[],"year":2021,"date_issued":"2021-05-01T07:00:00Z","date_published":"2021-05-01T07:00:00Z","updated_at":"2026-07-24T00:58:44Z","subjects":["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"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://scholarworks.uark.edu/etd/4048","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Mantooth, H. Alan","Chen, Zhong"]},{"key":"dc:contributor.advisor","label":"Advisor","values":["Dix, Jeff"]},{"key":"dc:creator","label":"Author","values":["Vincent, Luke"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2021"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2021-07-22T07:00:00Z"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science in Electrical Engineering (MSEE)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["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"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://scholarworks.uark.edu/etd/4048"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["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.
"]},{"key":"dc:title","label":"Title","values":["Analog Spiking Neural Network Implementing Spike Timing-Dependent Plasticity on 65 nm CMOS"]}]}],"canonical_facts":{"dc:contributor":["Mantooth, H. Alan","Chen, Zhong"],"dc:contributor.advisor":["Dix, Jeff"],"dc:creator":["Vincent, Luke"],"dc:date":["2021"],"dc:date.available":["2021-07-22T07:00:00Z"],"dc:description.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.
"],"dc:identifier":["https://scholarworks.uark.edu/etd/4048"],"dc:subject":["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"],"dc:title":["Analog Spiking Neural Network Implementing Spike Timing-Dependent Plasticity on 65 nm CMOS"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["Master of Science in Electrical Engineering (MSEE)"]},"updated_at":"2026-07-24T00:58:44Z"}