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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{
  "publisher": "Zenodo", 
  "DOI": "10.5281/zenodo.5301585", 
  "author": [
    {
      "family": "Bent, Graham"
    }, 
    {
      "family": "Simpkin, Christopher"
    }, 
    {
      "family": "Taylor, Ian"
    }, 
    {
      "family": "Rahimi, Abbas"
    }, 
    {
      "family": "Karunaratne, Geethan"
    }, 
    {
      "family": "Sebastian, Abu"
    }, 
    {
      "family": "Millar, Declan"
    }, 
    {
      "family": "Martens, Andreas"
    }, 
    {
      "family": "Roy, Kaushik"
    }
  ], 
  "issued": {
    "date-parts": [
      [
        2021, 
        4, 
        12
      ]
    ]
  }, 
  "abstract": "<p>Paper Abstract</p>\n\n<p>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 &lsquo;Distributed Federated Brain&#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 &lsquo;In Memory&#39; and &lsquo;Near Memory&#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 &lsquo;edge of network&#39; tactical MDO operations.</p>", 
  "title": "Energy efficient 'in memory' computing to enable decentralised service workflow composition in support of multi-domain operations", 
  "type": "paper-conference", 
  "id": "5301585"
}
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