Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/245506
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dc.titleTOWARD A RUNTIME PROGRAMMABLE SPIKING NEURAL NETWORK HARDWARE ACCELERATOR WITH ON-CHIP LEARNING
dc.contributor.authorNGUYEN NGOC NHU THAO
dc.date.accessioned2023-10-25T18:01:12Z
dc.date.available2023-10-25T18:01:12Z
dc.date.issued2023-02-28
dc.identifier.citationNGUYEN NGOC NHU THAO (2023-02-28). TOWARD A RUNTIME PROGRAMMABLE SPIKING NEURAL NETWORK HARDWARE ACCELERATOR WITH ON-CHIP LEARNING. ScholarBank@NUS Repository.
dc.identifier.urihttps://scholarbank.nus.edu.sg/handle/10635/245506
dc.description.abstractSignificant advances in neuroscience research have led to the emergence of Spiking Neural Networks (SNNs), energy-efficient machine learning algorithms inspired by the activities of the biological brain. Due to their event-driven nature, SNNs are expected to consume less energy than Artificial Neural Networks (ANNs). However, implementing complex SNNs that achieve satisfactory accuracy in various applications still demands substantial hardware resources, time, and energy. This thesis focuses on developing a hardware architecture and software-based methods to enable efficient implementation of SNNs with on-chip learning capability on embedded systems platforms. To achieve this objective, we propose a flexible SNN co-processor that allows runtime network reconfiguration. Moreover, a synapse pruning algorithm is introduced to reduce network complexity, resulting in significant time and energy savings on our hardware implementation. Additionally, we present a hardware-friendly semi-supervised learning scheme designed to facilitate online adaptation on embedded systems platforms.
dc.language.isoen
dc.subjectSpiking Neural Networks, Neuromorphic Computing, Synapse Pruning, Semi-Supervised Learning, On-Chip Learning, Reconfigurable Hardware Architecture
dc.typeThesis
dc.contributor.departmentELECTRICAL & COMPUTER ENGINEERING
dc.contributor.supervisorXuanyao Fong
dc.contributor.supervisorBharadwaj Veeravalli
dc.description.degreePh.D
dc.description.degreeconferredDOCTOR OF PHILOSOPHY (CDE-ENG)
dc.identifier.orcid0000-0002-9465-5694
Appears in Collections:Ph.D Theses (Open)

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