research-article

SpiNNaker: impact of traffic locality, causality and burstiness on the performance of the interconnection network

Authors:

Javier Navaridas,

Jose Miguel-Alonso,

Steve B. FurberAuthors Info & Claims

CF '10: Proceedings of the 7th ACM international conference on Computing frontiers

Pages 11 - 20

https://doi.org/10.1145/1787275.1787278

Published: 17 May 2010 Publication History

Abstract

The SpiNNaker system is a biologically-inspired massively parallel architecture of bespoke multi-core System-on-Chips. The aim of its design is to simulate up to a billion spiking neurons in (biological) real-time. Packets, in SpiNNaker, represent neural spikes and these travel through the two-dimensional triangular torus network that connects the over 65 thousand nodes housed in the largest size of SpiNNaker.

The research question that we explore is the impact that spatial locality, temporal causality and burstiness of the traffic have on the performance of such interconnection network. Given the limited knowledge of neuron activity patterns, we propose and use synthetic traffic patterns which resemble biological neural traffic and allow tuning of spatial locality. Causality is explored by means of temporal patterns that maintain a specified overall network load while allowing at the node level autonomous causal traffic generation. Part of the traffic is generated automatically, but the remaining traffic is triggered by a spike arrival in the form of a packet or a burst of packets; as neural stimuli do. In this way, we generate non-uniform traffic patterns with an evolving concentration of activity at nodes which contain more active parts of the spiking neural network.

Given the application domain, the simulation-based study focuses on the real-time behavior of the system rather than focusing on standard HPC network metrics. The results show that the interconnection network of SpiNNaker can operate without dropping packets with traffic loads that exceed more than 3.5 times those required to simulate 10⁹ spiking neurons, despite using non-local traffic. We also find that increments in the degree of traffic causality do not affect the performance of the system, but burstiness in the traffic can hurt performance.

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Cited By

Huang JKelber FVogginger BWu BKreutz FGerhards PScholz DKnobloch KMayr C(2023)Efficient Algorithms for Accelerating Spiking Neural Networks on MAC Array of SpiNNaker 22023 IEEE 5th International Conference on Artificial Intelligence Circuits and Systems (AICAS)10.1109/AICAS57966.2023.10168559(1-5)Online publication date: 11-Jun-2023
https://doi.org/10.1109/AICAS57966.2023.10168559
Shrestha AFang HMei ZRider DWu QQiu Q(2022)A Survey on Neuromorphic Computing: Models and HardwareIEEE Circuits and Systems Magazine10.1109/MCAS.2022.316633122:2(6-35)Online publication date: Oct-2023
https://doi.org/10.1109/MCAS.2022.3166331
Dytckov SPurohit SDaneshtalab MPlosila JTenhunen H(2014)Exploring NoC jitter effect on simulation of spiking neural networks2014 International Conference on High Performance Computing & Simulation (HPCS)10.1109/HPCSim.2014.6903756(693-696)Online publication date: Jul-2014
https://doi.org/10.1109/HPCSim.2014.6903756
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SpiNNaker: impact of traffic locality, causality and burstiness on the performance of the interconnection network

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Published In

cover image ACM Conferences

CF '10: Proceedings of the 7th ACM international conference on Computing frontiers

May 2010

370 pages

ISBN:9781450300445

DOI:10.1145/1787275

General Chair:
Nancy M. Amato
Texas A&M University, USA
,
Program Chairs:
Hubertus Franke
IBM Research, USA
,
Paul H.J. Kelly
Imperial College London, UK

Copyright © 2010 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 17 May 2010

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May 17 - 19, 2010

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Overall Acceptance Rate 273 of 785 submissions, 35%

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Cited By

Huang JKelber FVogginger BWu BKreutz FGerhards PScholz DKnobloch KMayr C(2023)Efficient Algorithms for Accelerating Spiking Neural Networks on MAC Array of SpiNNaker 22023 IEEE 5th International Conference on Artificial Intelligence Circuits and Systems (AICAS)10.1109/AICAS57966.2023.10168559(1-5)Online publication date: 11-Jun-2023
https://doi.org/10.1109/AICAS57966.2023.10168559
Shrestha AFang HMei ZRider DWu QQiu Q(2022)A Survey on Neuromorphic Computing: Models and HardwareIEEE Circuits and Systems Magazine10.1109/MCAS.2022.316633122:2(6-35)Online publication date: Oct-2023
https://doi.org/10.1109/MCAS.2022.3166331
Dytckov SPurohit SDaneshtalab MPlosila JTenhunen H(2014)Exploring NoC jitter effect on simulation of spiking neural networks2014 International Conference on High Performance Computing & Simulation (HPCS)10.1109/HPCSim.2014.6903756(693-696)Online publication date: Jul-2014
https://doi.org/10.1109/HPCSim.2014.6903756
Tse JLines A(2013)NanoMeshProceedings of the 2013 IEEE 19th International Symposium on Asynchronous Circuits and Systems10.1109/ASYNC.2013.17(40-49)Online publication date: 19-May-2013
https://dl.acm.org/doi/10.1109/ASYNC.2013.17
Navaridas JMiguel-Alonso JPascual JRidruejo F(2011)Simulating and evaluating interconnection networks with INSEESimulation Modelling Practice and Theory10.1016/j.simpat.2010.08.00819:1(494-515)Online publication date: Jan-2011
https://doi.org/10.1016/j.simpat.2010.08.008
Rast ANavaridas JJin XGalluppi FPlana LMiguel-Alonso JPatterson CLuján MFurber S(2011)Managing Burstiness and Scalability in Event-Driven Models on the SpiNNaker Neuromimetic SystemInternational Journal of Parallel Programming10.1007/s10766-011-0180-740:6(553-582)Online publication date: 23-Jul-2011
https://doi.org/10.1007/s10766-011-0180-7

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