Energy efficient spiking neural network neuromorphic processing to enable decentralised service workflow composition in support of multi-domain operations

Bent, Graham, Simpkin, Christopher, Li, Yuhua ORCID: https://orcid.org/0000-0003-2913-4478 and Preece, Alun ORCID: https://orcid.org/0000-0003-0349-9057 2022. Energy efficient spiking neural network neuromorphic processing to enable decentralised service workflow composition in support of multi-domain operations. Presented at: SPIE Defense + Commercial Sensing 2022, Orlando, Florida, United States, 3 April - 13 June 2022. Published in: Pham, Tien and Solomon, Latasha eds. Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications. , vol.12113 SPIE, 121131M. 10.1117/12.2617362

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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 workflow configurations, thereby creating the applications necessary to support changing mission needs. Motivated by neuromorphic processing models, in previous work it was shown that this can be achieved by using hyperdimensional symbolic semantic vector representations of the services/devices and workflows. Using a process of vector exchange the required services are dynamically discovered and inter-connected to achieve the required tasks. In network edge environments, the capability to perform these tasks with minimum energy consumption is critical. This paper describes how emerging spiking neural network (SNN) neuromorphic processing devices can be used to perform the required hyperdimensional vector computation (HDC) with significant energy savings compared to what can be achieved using traditional CMOS implementations.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics Data Innovation Research Institute (DIURI) Crime and Security Research Institute (CSURI)
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Uncontrolled Keywords:	Spiking Neural Networks, Service Discovery, Workflow Composition, Hyperdimensional Computing, Vector Symbolic Architecture
Publisher:	SPIE
Funders:	U.S. Army Research Laboratory, U.K. Ministry of Defence
Date of First Compliant Deposit:	10 June 2022
Date of Acceptance:	15 March 2022
Last Modified:	06 May 2023 02:03
URI:	https://orca.cardiff.ac.uk/id/eprint/150345

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