The modelling of large systems of spiking neurons is computationally very demanding in terms of processing power and communication. SpiNNaker – Spiking Neural Network architecture – is a massively parallel computer system designed to provide a cost-effective and flexible simulator for neuroscience experiments.
It can model up to a billion neurons and a trillion synapses in biological real time. The basic building block is the SpiNNaker Chip Multiprocessor (CMP), which is a custom-designed globally asynchronous locally synchronous (GALS) system with 18 ARM968 processor nodes residing in synchronous islands, surrounded by a lightweight, packet-switched asynchronous communications infrastructure.
In this paper, we review the design requirements for its very demanding target application, the SpiNNaker micro-architecture and its implementation issues. We also evaluate the SpiNNaker CMP, which contains 100 million transistors in a 102-mm2 die, provides a peak performance of 3.96 GIPS, and has a peak power consumption of 1 W when all processor cores operate at the nominal frequency of 180 MHz. SpiNNaker chips are fully operational and meet their power and performance requirements.
Authors: Eustace Painkras | Luis A. Plana | Jim Garside | Steve Temple | Francesco Galluppi | Cameron Patterson
| David R. Lester | Andrew D. Brown | Steve B. Furber