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Analytical Assessment of the Suitability of Multicast Communications for the SpiNNaker Neuromimetic System

Javier Navaridas, Mikel Luján, Luis A. Plana , Jose Miguel-Alonso , Steve B. Furber


SpiNNaker is a custom-made architecture designed to model large-scale spiking neural nets. One of the most significant characteristics of neural nets is their extreme communication needs; each neuron propagates its activation to thousands of other neurons. This paper shows analytical proof that the novel multicast router in SpiNNaker is a better solution for simulating neural nets than more powerful point-to-point routers such as those found on datacentres or high performance computing systems, even when it has significantly lower requirements in terms of complexity, area and power. First, we characterised the utilization of resources required by both multicast and unicast networks. Then we derived the bandwidth needs of different communication architectures. Finally, we derived the amount of neurons the different networks can support. From these analyses we determined that the multicast communications adopted in SpiNNaker will be able to support the target application under the expected operation conditions.