Conference paper Open Access

Energy efficient 'in memory' computing to enable decentralised service workflow composition in support of multi-domain operations

Bent, Graham; Simpkin, Christopher; Taylor, Ian; Rahimi, Abbas; Karunaratne, Geethan; Sebastian, Abu; Millar, Declan; Martens, Andreas; Roy, Kaushik


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    <dct:title>Energy efficient 'in memory' computing to enable decentralised service workflow composition in support of multi-domain operations</dct:title>
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    <dct:description>&lt;p&gt;Paper Abstract&lt;/p&gt; &lt;p&gt;Future Multi-Domain Operations (MDO) will require the coordination of hundreds, even thousands, of devices and component services. This will demand the capability to rapidly discover the distributed devices/services and combine them into different work ow configurations, thereby creating the applications necessary to support changing mission needs. To meet these objectives, we envision a distributed Cognitive Computing System (CCS) that consists of humans and software that work together as a &amp;lsquo;Distributed Federated Brain&amp;#39;. Motivated by neuromorphic processing models, we present an approach that uses hyper-dimensional symbolic semantic vector representations of the services/devices and workflows. We show how these can be used to perform decentralized service/device discovery and work ow composition in the context of a dynamic communications re-planning scenario. In this paper, we describe how emerging analogue AI &amp;lsquo;In Memory&amp;#39; and &amp;lsquo;Near Memory&amp;#39; computing devices can be used to efficiently perform some of the required hyper-dimensional vector computation (HDC). We present an evaluation of the performance of an energy-efficient phase change memory device (PCM) that can perform the required vector operations and discuss how such devices could be used in energy-critical &amp;lsquo;edge of network&amp;#39; tactical MDO operations.&lt;/p&gt;</dct:description>
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