DocumentCode :
20342
Title :
Overview of the SpiNNaker System Architecture
Author :
Furber, Steve B. ; Lester, David R. ; Plana, Luis A. ; Garside, Jim D. ; Painkras, Eustace ; Temple, Sally ; Brown, Andrew D.
Author_Institution :
Sch. of Comput. Sci., Univ. of Manchester, Manchester, UK
Volume :
62
Issue :
12
fYear :
2013
fDate :
Dec. 2013
Firstpage :
2454
Lastpage :
2467
Abstract :
SpiNNaker (a contraction of Spiking Neural Network Architecture) is a million-core computing engine whose flagship goal is to be able to simulate the behavior of aggregates of up to a billion neurons in real time. It consists of an array of ARM9 cores, communicating via packets carried by a custom interconnect fabric. The packets are small (40 or 72 bits), and their transmission is brokered entirely by hardware, giving the overall engine an extremely high bisection bandwidth of over 5 billion packets/s. Three of the principal axioms of parallel machine design (memory coherence, synchronicity, and determinism) have been discarded in the design without, surprisingly, compromising the ability to perform meaningful computations. A further attribute of the system is the acknowledgment, from the initial design stages, that the sheer size of the implementation will make component failures an inevitable aspect of day-to-day operation, and fault detection and recovery mechanisms have been built into the system at many levels of abstraction. This paper describes the architecture of the machine and outlines the underlying design philosophy; software and applications are to be described in detail elsewhere, and only introduced in passing here as necessary to illuminate the description.
Keywords :
failure analysis; microprocessor chips; neural net architecture; parallel machines; synchronisation; ARM9 cores; SpiNNaker system architecture; billion neurons; bisection bandwidth; component failures; custom interconnect fabric; day-to-day operation; design philosophy; determinism; fault detection; flagship goal; memory coherence; million-core computing engine; parallel machine design; principal axioms; recovery mechanisms; spiking neural network architecture; synchronicity; Biological system modeling; Computer architecture; Network architecture; Neural networks; Program processors; Interconnection architectures; neurocomputers; parallel processors; real-time distributed;
fLanguage :
English
Journal_Title :
Computers, IEEE Transactions on
Publisher :
ieee
ISSN :
0018-9340
Type :
jour
DOI :
10.1109/TC.2012.142
Filename :
6226357
Link To Document :
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