Neuromorphic technologies for defence and security

Kirkland, Paul and Di Caterina, Gaetano and Soraghan, John and Matich, George; Buller, Gerald S. and Hollins, Richard C. and Lamb, Robert A. and Laurenzis, Martin and Camposeo, Andrea and Farsari, Maria and Persano, Luana and Busse, Lynda E., eds. (2020) Neuromorphic technologies for defence and security. In: Emerging Imaging and Sensing Technologies for Security and Defence V; and Advanced Manufacturing Technologies for Micro- and Nanosystems in Security and Defence III. Proceedings of SPIE . SPIE, Bellingham, Washington. ISBN 9781510638945 (https://doi.org/10.1117/12.2575978)

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Abstract

Despite the highly promising advances in Machine Learning (ML) and Deep Learning (DL) in recent years, DL requires significant hardware acceleration to be effective, as it is rather computationally expensive. Moreover, miniaturisation of electronic devices requires small form-factor processing units, with reduced SWaP (Size,Weight and Power) profile. Therefore, a completely new processing paradigm is needed to address both issues. In this context, the concept of neuromorphic (NM) engineering provides an attractive alternative, seen as the analog/digital implementation of biologically brain inspired neural networks. NM systems propagate spikes as means of processing data, with the information being encoded in the timing and rate of spikes generated by each neuron of a so-called spiking neural network (SNN). Based on this, the key advantages of SNNs are: less computational power required, more efficient and faster processing, much lower power consumption. This paper reports on the current state of the art in the field of NM systems, and it describes three application scenarios of SNN-based processing for security and defence, namely target detection and tracking, semantic segmentation, and control.

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