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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  <dc:creator>Bent, Graham</dc:creator>
  <dc:creator>Simpkin, Christopher</dc:creator>
  <dc:creator>Taylor, Ian</dc:creator>
  <dc:creator>Rahimi, Abbas</dc:creator>
  <dc:creator>Karunaratne, Geethan</dc:creator>
  <dc:creator>Sebastian, Abu</dc:creator>
  <dc:creator>Millar, Declan</dc:creator>
  <dc:creator>Martens, Andreas</dc:creator>
  <dc:creator>Roy, Kaushik</dc:creator>
  <dc:date>2021-04-12</dc:date>
  <dc:description>Paper Abstract

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 ‘Distributed Federated Brain'. 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 ‘In Memory' and ‘Near Memory' 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 ‘edge of network' tactical MDO operations.</dc:description>
  <dc:identifier>https://zenodo.org/record/5301585</dc:identifier>
  <dc:identifier>10.5281/zenodo.5301585</dc:identifier>
  <dc:identifier>oai:zenodo.org:5301585</dc:identifier>
  <dc:relation>info:eu-repo/grantAgreement/EC/H2020/682675/</dc:relation>
  <dc:relation>doi:10.5281/zenodo.5301584</dc:relation>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>https://creativecommons.org/licenses/by/4.0/legalcode</dc:rights>
  <dc:title>Energy efficient 'in memory' computing to enable decentralised service workflow composition in support of multi-domain operations</dc:title>
  <dc:type>info:eu-repo/semantics/conferencePaper</dc:type>
  <dc:type>publication-conferencepaper</dc:type>
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