Social-Insect-Inspired Networking for Autonomous Fault Tolerance

Rowlings, Matthew; Tyrrell, Andy M.; Trefzer, Martin A.

doi:10.1109/ssci.2015.172

Cited by 5 publications

(1 citation statement)

References 10 publications

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“…Experiments aim to capture the relevant decision processes made by each member of the colony to exhibit such high-level behaviours and embed these decision engines within the routers of the many-core system. Results with the bespoke 128-core Centurion platform suggest that there is potential for the social insect model as a distributed, embedded intelligence within a many-core system and with the relevant knobs and monitors, such as packet routing events, timing violation detection, router behaviour, clock frequency and temperature, to close the loop for emergent autonomous adaptation and fault tolerance [3], [4].…”

Section: Introductionmentioning

confidence: 99%

Embedded Social Insect-Inspired Intelligence Networks for System-level Runtime Management

Rowlings

Tyrrell

Trefzer

2020

2020 Design, Automation &Amp; Test in Europe Conference &Amp; Exhibition (DATE)

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Large-scale distributed computing architectures such as, e.g. systems on chip or many-core devices, offer advantages over monolithic or centralised single-core systems in terms of speed, power/thermal performance and fault tolerance. However, these are not implicit properties of such systems and runtime management at software or hardware level is required to unlock these features. Biological systems naturally present such properties and are also adaptive and scalable. To consider how these can be similarly achieved in hardware may be beneficial. We present Social Insect behaviours as a suitable model for enabling autonomous runtime management (RTM) in many-core architectures. The emergent properties sought to establish are self-organisation of task mapping and systemlevel fault tolerance. For example, large social insect colonies accomplish a wide range of tasks to build and maintain the colony. Many thousands of individuals, each possessing relatively little intelligence, contribute without any centralised control. Hence, it would seem that social insects have evolved a scalable approach to task allocation, load balancing and robustness that can be applied to large many-core computing systems. Based on this, a self-optimising and adaptive, yet fundamentally scalable, design approach for many-core systems based on the emergent behaviours of social-insect colonies are developed. Experiments capture decision-making processes of each colony member to exhibit such high-level behaviours and embed these decision engines within the routers of the many-core system.

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Section: Introductionmentioning

confidence: 99%

Embedded Social Insect-Inspired Intelligence Networks for System-level Runtime Management

Rowlings

Tyrrell

Trefzer

2020

2020 Design, Automation &Amp; Test in Europe Conference &Amp; Exhibition (DATE)

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Social-insect-inspired adaptive task allocation for many-core systems

Rowlings

Tyrrell

Trefzer

2016

2016 IEEE Congress on Evolutionary Computation (CEC)

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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.

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