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

doi:10.1117/12.821682

20 May 2009 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, Bioengineered and Bioinspired Systems IV; 73650M (2009) https://doi.org/10.1117/12.821682
Event: SPIE Europe Microtechnologies for the New Millennium, 2009, Dresden, Germany

Abstract

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.

Citation Download Citation

B. Belhadj, J. Tomas, O. Malot, Y. Bornat, G. N'Kaoua, and S. Renaud "Token-passing communication protocol in hardware-based real-time spiking neural networks", Proc. SPIE 7365, Bioengineered and Bioinspired Systems IV, 73650M (20 May 2009); https://doi.org/10.1117/12.821682

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