Implementation of event-driven spiking neural networks in field programmable gate array (FPGA)

Spiking Neuron Networks (SNNs) are a fascinating new field of artificial intelligence and computational neuroscience that is directly inspired by the complex work of biological brain systems. Unlike traditional feedforward neural networks (FNN) and recurrent neural networks (RNN), which rely on cont...

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Main Author: Yuan, Chenhao
Other Authors: Gwee Bah Hwee
Format: Thesis-Master by Coursework
Language:English
Published: Nanyang Technological University 2024
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Online Access:https://hdl.handle.net/10356/177902
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1779022024-06-07T15:43:32Z Implementation of event-driven spiking neural networks in field programmable gate array (FPGA) Yuan, Chenhao Gwee Bah Hwee School of Electrical and Electronic Engineering ebhgwee@ntu.edu.sg Engineering Spiking neural networks Machine learning Event-driven FPGA Spiking Neuron Networks (SNNs) are a fascinating new field of artificial intelligence and computational neuroscience that is directly inspired by the complex work of biological brain systems. Unlike traditional feedforward neural networks (FNN) and recurrent neural networks (RNN), which rely on continuous data streams and feedback for learning, SNN uses an innovative information processing and transmission method. They use discrete synaptic events called spikes to communicate between neurons, which is very similar to pulse-based communication in the human brain and nervous system. In this dissertation, based on human neuron system and basic principles of SNN. We develop a simulation system for SNN data processing from input to output, simultaneously validating the network structure and neuron parameters through software-level training optimization. Through our work, we find that SNN has practical usage in the field of image recognition. So we continue to try to implement SNN in hardware-level, aiming to leverage the unique characteristics of hardware architectures to unlock new capabilities and efficiencies. Master's degree 2024-06-03T05:28:36Z 2024-06-03T05:28:36Z 2024 Thesis-Master by Coursework Yuan, C. (2024). Implementation of event-driven spiking neural networks in field programmable gate array (FPGA). Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/177902 https://hdl.handle.net/10356/177902 en application/pdf Nanyang Technological University
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering
Spiking neural networks
Machine learning
Event-driven
FPGA
spellingShingle Engineering
Spiking neural networks
Machine learning
Event-driven
FPGA
Yuan, Chenhao
Implementation of event-driven spiking neural networks in field programmable gate array (FPGA)
description Spiking Neuron Networks (SNNs) are a fascinating new field of artificial intelligence and computational neuroscience that is directly inspired by the complex work of biological brain systems. Unlike traditional feedforward neural networks (FNN) and recurrent neural networks (RNN), which rely on continuous data streams and feedback for learning, SNN uses an innovative information processing and transmission method. They use discrete synaptic events called spikes to communicate between neurons, which is very similar to pulse-based communication in the human brain and nervous system. In this dissertation, based on human neuron system and basic principles of SNN. We develop a simulation system for SNN data processing from input to output, simultaneously validating the network structure and neuron parameters through software-level training optimization. Through our work, we find that SNN has practical usage in the field of image recognition. So we continue to try to implement SNN in hardware-level, aiming to leverage the unique characteristics of hardware architectures to unlock new capabilities and efficiencies.
author2 Gwee Bah Hwee
author_facet Gwee Bah Hwee
Yuan, Chenhao
format Thesis-Master by Coursework
author Yuan, Chenhao
author_sort Yuan, Chenhao
title Implementation of event-driven spiking neural networks in field programmable gate array (FPGA)
title_short Implementation of event-driven spiking neural networks in field programmable gate array (FPGA)
title_full Implementation of event-driven spiking neural networks in field programmable gate array (FPGA)
title_fullStr Implementation of event-driven spiking neural networks in field programmable gate array (FPGA)
title_full_unstemmed Implementation of event-driven spiking neural networks in field programmable gate array (FPGA)
title_sort implementation of event-driven spiking neural networks in field programmable gate array (fpga)
publisher Nanyang Technological University
publishDate 2024
url https://hdl.handle.net/10356/177902
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