Power analysis of large-scale, real-time neural networks on SpiNNaker

Evangelos Stromatias, Francesco Galluppi, Cameron Patterson, Stephen Furber

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Simulating large spiking neural networks is non trivial: supercomputers offer great flexibility at the price of power and communication overheads; custom neuromorphic circuits are more power efficient but less flexible; while alternative approaches based on GPGPUs and FPGAs, whilst being more readily available, show similar model specialization. As well as efficiency and flexibility, real time simulation is a desirable neural network characteristic, for example in cognitive robotics where embodied agents interact with the environment using low-power, event-based neuromorphic sensors. The SpiNNaker neuromimetic architecture has been designed to address these requirements, simulating large-scale heterogeneous models of spiking neurons in real-time, offering a unique combination of flexibility, scalability and power efficiency. In this work a 48-chip board is utilised to generate a SpiNNaker power estimation model, based on numbers of neurons, synapses and their firing rates. In addition, we demonstrate simulations capable of handling up to a quarter of a million neurons, 81 million synapses and 1.8 billion synaptic events per second, with the most complex simulations consuming less than 1 Watt per SpiNNaker chip. © 2013 IEEE.
Original languageEnglish
Title of host publicationProceedings of the International Joint Conference on Neural Networks|Proc Int Jt Conf Neural Networks
Place of PublicationUSA
PublisherIEEE
Pages1570-1577
Number of pages8
ISBN (Print)9781467361293
DOIs
Publication statusPublished - 4 Aug 2013
Event2013 International Joint Conference on Neural Networks, IJCNN 2013 - Dallas, TX
Duration: 1 Jul 2013 → …

Conference

Conference2013 International Joint Conference on Neural Networks, IJCNN 2013
CityDallas, TX
Period1/07/13 → …

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