Social-insect-inspired adaptive task allocation for many-core systems
Social-insect-inspired adaptive task allocation for many-core systems
复制标题
受社会昆虫启发的多核系统自适应任务分配
DOI：
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
M. Trefzer
中科院分区：
文献类型：
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作者：
M. Rowlings;A. Tyrrell;M. Trefzer
大型社会性昆虫群体需要承担广泛的重要任务来建立和维持群体。幸运的是，在大多数巢穴中，都有成千上万的工蚁可以提供帮助，以确保蚁群的扩张和生存。然而，执行每项任务的工人数量之间存在着至关重要的平衡，不仅必须保持这种平衡，而且还必须不断适应环境和群体需求的突然变化。最令人着迷的是，群居昆虫可以在没有任何集中控制的情况下维持这种平衡，并且群体成员的智力相对较低。由于这种简单性和明显的可扩展性，社会性昆虫似乎已经进化出一种有趣的可扩展的任务分配方法，可以应用于非常大的多核系统。为了研究这一点，我们探索了蚁群中任务分配的生物模型，并将其应用于 36 核片上网络。本文不仅表明实现了有效的分散任务分配，而且表明这种方案可以适应故障并改变其行为以满足软实时约束。因此，可以确定的是，受社会昆虫启发的智能模型为以分散和自适应的方式应对暗硅和现场故障带来的挑战提供了合适的隐喻和发展方向。
Large social insect colonies require a wide range of important tasks to be undertaken to build and maintain the colony. Fortunately, in most nests there are many thousands of workers available to offer their assistance to ensure the expansion and survival of the colony. However, there is a crucial equilibrium between the number of workers performing each task that must not only be maintained but must also continuously adapt to sudden changes in environment and colony need. What is most fascinating is that social insects can sustain this balance without any centralised control and with colony members that have relatively little intelligence when considered on their own. Due to this simplicity and evident scalability it would seem that social insects have evolved an interesting scalable approach to task allocation that could be applied to very large many-core systems. To investigate this we have explored biological models of task allocation in ant colonies and applied this to a 36-core Network on Chip. This paper not only shows that effective decentralised task allocation is achieved, but also that such a scheme can adapt to faults and alter its behaviour to meet soft real-time constraints. Therefore, it is established that social insect inspired intelligence models offer a suitable metaphor and development direction for tackling the challenges introduced by dark silicon and in-field faults in a decentralised and adaptive fashion.