SpiNNaker: Event-based simulation - Quantitative behavior

• Main Authors: Brown, Andrew D., Chad, John E., Kamarudin, Muhammad Raihaan, Dugan, Kier J., Furber, Stephen B.
• Format: Article
• Language: English
• Published: Institute Of Electrical And Electronics Engineers Inc. (IEEE) 2018
• Online Access: http://eprints.utem.edu.my/id/eprint/22902/2/SpiNNaker%20Event%20Based%20Simulation%20-%20Quantitative%20Behaviour.pdf; http://eprints.utem.edu.my/id/eprint/22902/; https://ieeexplore.ieee.org/abstract/document/8118143
• Institution: Universiti Teknikal Malaysia Melaka

SpiNNaker (Spiking Neural Network Architecture) is a specialized 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 behavior 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 nine million synaptic connections on a 768 core subsystem (�1400th of the full system) to accurately produce logically predicted results.