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- short-paperMay 2018
Network-on-chip evaluation for a novel neural architecture
CF '18: Proceedings of the 15th ACM International Conference on Computing FrontiersPages 216–219https://doi.org/10.1145/3203217.3203268This paper provides a performance evaluation and trade-off analysis of a novel chip architecture for neuromorphic computing, especially focused on the memory subsystems and the Network-On-Chip (NoC). More precisely, we study the performance-related ...
- research-articleMay 2014
On generating multicast routes for SpiNNaker
CF '14: Proceedings of the 11th ACM Conference on Computing FrontiersArticle No.: 2, Pages 1–10https://doi.org/10.1145/2597917.2597938The human brain is an immense biological neural network characterized by high degrees of connectivity among neurons. Any system designed to simulate biologically-plausible spiking neuronal networks needs to support such connectivity and the associated ...
- research-articleMay 2012
A hierachical configuration system for a massively parallel neural hardware platform
CF '12: Proceedings of the 9th conference on Computing FrontiersPages 183–192https://doi.org/10.1145/2212908.2212934Simulation of large networks of neurons is a powerful and increasingly prominent methodology for investigate brain functions and structures. Dedicated parallel hardware is a natural candidate for simulating the dynamic activity of many non-linear units ...
- research-articleMay 2011
Maintaining real-time synchrony on SpiNNaker
CF '11: Proceedings of the 8th ACM International Conference on Computing FrontiersArticle No.: 15, Pages 1–2https://doi.org/10.1145/2016604.2016622As an asynchronous universal multiprocessor for real-time neural simulation, SpiNNaker presents timing concerns not present in synchronous systems. In this paper we present a series of tools that solve the problem of synchronising a multichip ...
- posterMay 2010
Efficient parallel implementation of multilayer backpropagation networks on SpiNNaker
CF '10: Proceedings of the 7th ACM international conference on Computing frontiersPages 89–90https://doi.org/10.1145/1787275.1787297This paper presents an efficient implementation and performance analysis of mapping multilayer perceptron networks with the backpropagation learning rule on SpiNNaker - a massively parallel architecture dedicated for neural network simulation. A new ...
- posterMay 2010
A communication infrastructure for a million processor machine
- Andrew D. Brown,
- Steven B. Furber,
- Jeff S. Reeve,
- Peter R. Wilson,
- Mark Zwolinski,
- John E. Chad,
- Luis Plana,
- David R. Lester
CF '10: Proceedings of the 7th ACM international conference on Computing frontiersPages 75–76https://doi.org/10.1145/1787275.1787290SpiNNaker (Spiking Neural Network architecture) is a massively parallel computing machine, comprising a million ARM9 cores. These are realised on 50000 chips, 20 cores/chip. While it could be classed as a MIMD machine, there is no unifying bus structure,...
- research-articleMay 2010
Scalable event-driven native parallel processing: the SpiNNaker neuromimetic system
CF '10: Proceedings of the 7th ACM international conference on Computing frontiersPages 21–30https://doi.org/10.1145/1787275.1787279Neural networks present a fundamentally different model of computation from the conventional sequential digital model. Modelling large networks on conventional hardware thus tends to be inefficient if not impossible. Neither dedicated neural chips, with ...
- research-articleMay 2010
SpiNNaker: impact of traffic locality, causality and burstiness on the performance of the interconnection network
CF '10: Proceedings of the 7th ACM international conference on Computing frontiersPages 11–20https://doi.org/10.1145/1787275.1787278The 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 ...
