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12 April 2021 Energy efficient 'in memory' computing to enable decentralised service workflow composition in support of multi-domain operations
Graham Bent, Christopher Simpkin, Ian Taylor, Abbas Rahimi, Geethan Karunaratne, Abu Sebastian, Declan Millar, Andreas Martens, Kaushik Roy
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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.
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© (2021) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Graham Bent, Christopher Simpkin, Ian Taylor, Abbas Rahimi, Geethan Karunaratne, Abu Sebastian, Declan Millar, Andreas Martens, and Kaushik Roy "Energy efficient 'in memory' computing to enable decentralised service workflow composition in support of multi-domain operations", Proc. SPIE 11746, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications III, 117461Q (12 April 2021); https://doi.org/10.1117/12.2586988
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