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

Graham Bent; Christopher Simpkin; Yuhua Li; Alun Preece

doi:10.1117/12.2617362

6 June 2022 Energy efficient spiking neural network neuromorphic processing to enable decentralised service workflow composition in support of multi-domain operations

Graham Bent, Christopher Simpkin, Yuhua Li, Alun Preece

Author Affiliations +

Proceedings Volume 12113, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications IV; 121131M (2022) https://doi.org/10.1117/12.2617362
Event: SPIE Defense + Commercial Sensing, 2022, Orlando, Florida, United States

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.

Conference Presentation

Citation Download Citation

Graham Bent, Christopher Simpkin, Yuhua Li, and Alun Preece "Energy efficient spiking neural network neuromorphic processing to enable decentralised service workflow composition in support of multi-domain operations", Proc. SPIE 12113, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications IV, 121131M (6 June 2022); https://doi.org/10.1117/12.2617362

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