Hardware Implementation of Spiking Neural Networks

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Taji, Hossein | 2020

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Type of Document: M.Sc. Thesis
Language: Farsi
Document No: 52645 (05)
University: Sharif University of Technology
Department: Electrical Engineering
Advisor(s): Shabany, Mahdi; Hashemi, Matin
Abstract:
Spiking neural networks (SNNs) are third generation of neural networks. Similar to traditional neural networks, SNNs are comprised neurons. However, not only structure but also information processing is inspired by animal neural systems. SNNs can be called the most similar networks to animal neural systems. In such networks, the information is processed based on propagation of spike signals through the network. The type of data flow in these networks leads to being low-power when they are implemented on hardware. Therefore,there has been a upward trend in hardware implementation of them, like their FPGA implementations, for applications such as Big Data and Machine Learning. In this thesis, a method and an architecture for effcient hardware implementation of SNNs are proposed.Morover, unlike other works focusing on hardware accelerations for previously learned networks, the propoed architecture has ability to learn on-chip, i.e. training of the network weights are done on the chip
Keywords:
Hardware Implementation ; Field Programmable Gate Array (FPGA) ; Event-Driven Softwares ; Spiking Deep Convolutional Neural Networks ; On-Chip Learning

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