A. Mouraud, H. Paugam-Moisy, and D. Puzenat (France)
Spiking Neural Networks, Event-Driven Simulations, Par allel Computing, Multi-threading, Scheduling.
In a Spiking Neural Networks (SNN), spike emissions are sparsely and irregularly distributed both in time and in the network architecture. Since a current feature of SNNs is a low average activity, efficient implementations of SNNs are usually based on an Event-Driven Simulation (EDS). On the other hand, simulations of large scale neural net works can take advantage of distributing the neurons on a set of processors (either workstation cluster or parallel computer). This article presents a large scale SNN simula tion framework able to gather the benefits of EDS and par allel computing. Two levels of parallelism are combined: Distributed mapping of the neural topology, at the network level, and local multithreaded allocation of resources for si multaneous processing of events, at the neuron level. Based on the causality of events, a distributed solution is pro posed for solving the complex problem of scheduling with out synchronization barrier.
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