SpiNNaker: Event-based simulation - quantitative behaviour

Research output: Research - peer-reviewArticle

  • Authors:
  • Andrew D Brown
  • John E. Chad
  • Raihaan Kamarudin
  • K.J. Dugan
  • Stephen Furber


SpiNNaker (Spiking Neural Network Architecture) is a specialised computing engine, intended for real-time simulation of neural systems. It consists of a mesh of 240x240 nodes, each containing 18 ARM9 processors: over a million
cores, communicating via a bespoke network. Ultimately, the machine will support the simulation of up to a billion neurons in real time, allowing simulation experiments to be taken to hitherto unattainable scales. The architecture achieves this by ignoring three of the axioms of computer design: the communication fabric is non-deterministic; there is no global core synchronisation, and the system state - held in distributed memory - is not coherent. Time models itself: there is no notion of computed simulation time - wallclock time is simulation time. Whilst these design decisions are orthogonal to conventional wisdom, they bring the engine behaviour closer to its intended simulation target - neural systems. We describe how SpiNNaker simulates
large neural ensembles; we provide performance figures and outline some failure mechanisms. SpiNNaker simulation time scales 1:1 with wallclock time at least up to 9 million synaptic connections on a 768 core subsystem (~1400th of the full system) to accurately produce logically predicted results.

Bibliographical metadata

Original languageEnglish
JournalIEEE Transactions on Multi-Scale Computing Systems
Early online date22 Nov 2017
StatePublished - 2017