Distributed Configuration of Massively-Parallel Simulation on SpiNNaker Neuromorphic Hardware
Thomas Sharp, Cameron Patterson and Steve Furber
Abstract
SpiNNaker is a massively-parallel neuromorphic computing architecture designed to model very large, biologically plausible spiking neural networks in real-time. A SpiNNaker machine consists of up to 216 homogeneous eighteencore multiprocessor chips, each with an on-board router which forms links with neighbouring chips for packet-switched interprocessor communications. The architecture is designed for dynamic reconfiguration and optimised for transmission of neural activity data, which presents a challenge for machine configuration, program loading and simulation monitoring given a lack of globally-shared memory resources, intrinsic addressing mode or sideband configuration channel. We propose distributed software mechanisms to address these problems and present experiments which demonstrate the necessity of this approach in contrast to centralised mechanisms.