Mark Ogbodo の履歴(No.14)

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Fault-tolerant 3D-NoC-based Spiking Neuromorphic System/Chip with Light-weight On-chip Learning

Research Background†

Please complete this section

Research Goal†

The goal of this Doctoral thesis is to research and implement a Deep Spiking Neural Network Architecture with on-chip learning scheme and based on the previously developed schemes NASH1

The expected outputs from this research are:

(1) An efficient adaptive configuration method which enables reconfiguration of different SNN parameters (spike weights, routing, hidden layers, topology, etc.)
(2) An Efficient Multicast Fault-tolerant Routing Algorithm for the neurochip
(3) Efficient on-chip learning Algorithm
(4) To demonstrate the performance of the algorithms and the system, an FPGA implementation shall be developed and evalauted with several biological SNNs. In addition, a VLSI implementation shall be also developed.

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