NDLI: Optimising the overall power usage on the SpiNNaker neuromimetic platform

Content Provider	IEEE Xplore Digital Library
Author	Stromatias, E. Patterson, C. Furber, S.
Copyright Year	2014
Description	Author affiliation: Sch. of Comput. Sci., Univ. of Manchester, Manchester, UK (Stromatias, E.; Patterson, C.; Furber, S.)
Abstract	Simulations of biological tissue have been extensively used to replicate phenomena observed by in-vivo and in-vitro experiments as an alternative methodology for explaining how computations could take place in a brain region. Additional benefits of simulated neural networks over in-vivo experiments include greater observability, experimental control and reproducibility. General-purpose supercomputers provide the computational power and parallelism required to implement highly complex neural models, but this comes at the expense of high power requirements and communication overheads. Moreover, there are certain cases where real-time simulation performance is a desirable feature, for example in the field of cognitive robotics where embodied agents need to interact with their environment through biologically inspired asynchronous sensors. The SpiNNaker neuromimetic platform is a scalable architecture that has been designed to enable energy-efficient, large-scale simulations of spiking neurons in biological realtime. This work is based on a recent study which revealed that while they are generally energy efficient, SpiNNaker chips dissipate significant amount of power whilst in the idle state. In this paper we perform a systematic investigation into the overall energy consumption of a SpiNNaker system and propose a number of optimised suspend modes in order to reduce this. The proposed implementation is 60% more energy efficient in the idle state, 50% in the uploading and 52% in the downloading phases, while the power dissipation of the whole simulation is reduced by 52%. For demonstration purposes, we run a neural network simulation comprising thousands of neurons and millions of complex synapses on a 48-chip SpiNNaker board, generating millions of synaptic events per second.
Starting Page	4280
Ending Page	4287
File Size	1723643
Page Count	8
File Format	PDF
ISSN	21614407
e-ISBN	9781479914845
DOI	10.1109/IJCNN.2014.6889837
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-07-06
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neurons Biological system modeling Clocks Computational modeling Program processors Biological neural networks Integrated circuit modeling
Content Type	Text
Resource Type	Article

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