Mr. Bilel Belhadj-Mohamed Profile

Bilel Belhadj-Mohamed

at Univ Bordeaux I

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Proceedings Article | 20 May 2009 Paper

Token-passing communication protocol in hardware-based real-time spiking neural networks

B. Belhadj, J. Tomas, O. Malot, Y. Bornat, G. N'Kaoua, S. Renaud

Proceedings Volume 7365, 73650M (2009) https://doi.org/10.1117/12.821682

KEYWORDS: Neurons, Neural networks, Field programmable gate arrays, Silicon, Computer simulations, Telecommunications, Control systems, Analog electronics, Signal processing, Process modeling

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Biological neural networks are based upon axonal point-to-point connections which inspire connectionist architecture. As we attempt to engineer ever larger analogues of these neural networks we are forced to multiplex neural signals over time shared paths. This can alter timing of neural information, which is critical in real-time oscillatory networks. Because shared paths induce extra delay due to multiplexing signals, traveling on the channel and passing through routing devices, guaranteeing event arrival deadlines across the communication process becomes crucial. This paper addresses issues related to the guarantee of event timings with arbitrary deadline constraints in real-time distributed spiking neural network systems based on token-ring architecture. To achieve this objective, we propose an integrated method in selecting key system parameters. We show that several parameters must be set carefully if event deadlines are to be satisfied. The token holding time (THT) parameter controls the bandwidth allocation for each node in the token-ring network, and must be set properly to avoid deadline misses. The target token rotation time (TTRT) determines both the speed of token circulation and the network utilization available to nodes. TTRT should also be chosen carefully to ensure that the token circulates fast enough while maintaining a high available utilization. As prove of concept, the proposed method is applied to a multi-board spiking neural network system hosting up to 140 analog neurons spread across 7 circuit-boards. Experimental analysis shows that deadline constraints are guaranteed along with bandwidth allocation fairness when applying the proposed method.

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