Conference paper Embargoed 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

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.

Embargoed Access

Files are currently under embargo but will be publicly accessible after October 12, 2021.

10
1
views
downloads
All versions This version
Views 1010
Downloads 11
Data volume 5.7 MB5.7 MB
Unique views 1010
Unique downloads 11

Share

Cite as