University of Cambridge > Talks.cam > Computer Laboratory Computer Architecture Group Meeting > Communication Challenges for Extreme-Scale, Real-Time Neural Network Simulation

Communication Challenges for Extreme-Scale, Real-Time Neural Network Simulation

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Neural network simulation is an example of an embarrassingly-parallel problem with complex communication patterns. The neurons being simulated are inherently parallel but the spike messages between them exhibit complex patterns. We consider GPUs as a possible simulation platform, but discover that their methods of processing and communication make them unsuited to the task. As an alternative we propose a simulation platform consisting of a network of Field Programmable Gate Arrays (FPGAs), connected using integrated high-speed serial transceivers. This system is much more suited to the complex communication patterns and real-time requirements of an extreme-scale neural network simulation.

This talk is part of the Computer Laboratory Computer Architecture Group Meeting series.

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