分布式神经计算中的容错
Fault Tolerance in Distributed Neural Computing
摘要：
随着计算系统复杂性的增加，完全的硬件可靠性不再能得到保证。但是，我们需要确保整个系统的可靠性。人工神经网络最重要的特征之一是其内在的容错性。这项工作的目的是调查这种网络是否具有可以应用于更广泛的计算系统的功能。本文提出了一种分析，在学习和操作阶段，分布式前馈神经网络与分散的事件驱动的时间管理，这是不敏感的间歇性故障所造成的不可靠的通信或故障的硬件组件。模型中使用的学习规则在空间和时间上都是局部的，这允许有效的可扩展的分布式实现。我们调查注入故障引起的开销，并分析在网络的不同区域的计算硬件有限的故障的敏感性。
英文摘要：
With the increasing complexity of computing systems, complete hardware reliability can no longer be guaranteed. We need, however, to ensure overall system reliability. One of the most important features of artificial neural networks is their intrinsic fault-tolerance. The aim of this work is to investigate whether such networks have features that can be applied to wider computational systems. This paper presents an analysis, in both the learning and operational phases, of a distributed feed-forward neural network with decentralised event-driven time management, which is insensitive to intermittent faults caused by unreliable communication or faulty hardware components. The learning rules used in the model are local in space and time, which allows efficient scalable distributed implementation. We investigate the overhead caused by injected faults and analyse the sensitivity to limited failures in the computational hardware in different areas of the network.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
