Efficient Hardware Implementation of a Multi-Layer Gradient-Free Online-Trainable Spiking Neural Network on FPGA

Efficient Hardware Implementation of a Multi-Layer Gradient-Free Online-Trainable Spiking Neural Network on FPGA

This paper presents an efficient hardware implementation of the recently proposed Optimised Deep Event-driven Spiking Neural Network Architecture (ODESA). ODESA is the first network to have end-to-end multi-layer online local supervised training without using gradients and has the combined adaptatio...

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Bibliographic Details
Main Authors: Ali Mehrabi, Yeshwanth Bethi, Andre van Schaik, Andrew Wabnitz, Saeed Afshar
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Access
Subjects:
Spiking neural networks
supervised learning
neuromorphic hardware
field programmable gate array (FPGA)
Online Access:https://ieeexplore.ieee.org/document/10755039/
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https://ieeexplore.ieee.org/document/10755039/

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