On generating multicast routes for SpiNNaker

J. Navaridas, M. Luján, L. Plana, S. Temple, S. Furber
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引用次数: 4

Abstract

The 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 communication traffic in the form of spike events. This paper demonstrates the adequacy of multicast communications to achieve such a demanding goal and introduces a collection of algorithms to generate multicast routes. These algorithms target the SpiNNaker interconnect; a two dimensional triangular toroidal mesh with support for selective multicast. As generating multicast routes is a NP-complete problem, these algorithms are an essential ingredient for an efficient operation of SpiNNaker. Although multicast networks have been studied in the literature, existing algorithms cannot be applied efficiently to SpiNNaker. A comprehensive evaluation analyzing the largest configuration of the SpiNNaker system (over 1 million ARM cores) shows that each algorithm provides diverse benefits and drawbacks which can be exploited to avoid possible bottlenecks. Results show that the communication infrastructure of SpiNNaker will be able to support the high communication pressure exerted by simulating in real-time biologically plausible spiking neural applications
SpiNNaker多播路由生成研究
人脑是一个巨大的生物神经网络,其特征是神经元之间的高度连接。任何旨在模拟生物学上可信的脉冲神经网络的系统都需要支持这种连接以及以脉冲事件的形式相关的通信流量。本文论证了组播通信足以实现这一要求,并介绍了一组生成组播路由的算法。这些算法的目标是SpiNNaker互连;支持选择性组播的二维三角形环面网格。由于组播路由的生成是一个np完全问题,这些算法是SpiNNaker高效运行的重要组成部分。虽然已有文献对多播网络进行了研究,但现有算法不能有效地应用于SpiNNaker。一项综合评估分析了SpiNNaker系统的最大配置(超过100万个ARM内核),表明每种算法都有不同的优点和缺点,可以利用这些优点和缺点来避免可能的瓶颈。结果表明,SpiNNaker的通信基础设施能够通过实时模拟生物学上合理的尖峰神经应用来支持高通信压力
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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