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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| Format: | Thesis-Master by Coursework |
| Language: | English |
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Nanyang Technological University
2024
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| Online Access: | https://hdl.handle.net/10356/177902 |
| _version_ | 1811692005250564096 |
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| author | Yuan, Chenhao |
| author2 | Gwee Bah Hwee |
| author_facet | Gwee Bah Hwee Yuan, Chenhao |
| author_sort | Yuan, Chenhao |
| collection | NTU |
| 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. |
| first_indexed | 2024-10-01T06:28:54Z |
| format | Thesis-Master by Coursework |
| id | ntu-10356/177902 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T06:28:54Z |
| publishDate | 2024 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-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 |
| 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) |
| title | 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_short | 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 |
| topic | Engineering Spiking neural networks Machine learning Event-driven FPGA |
| url | https://hdl.handle.net/10356/177902 |
| work_keys_str_mv | AT yuanchenhao implementationofeventdrivenspikingneuralnetworksinfieldprogrammablegatearrayfpga |