Neuromorphic technologies for defence and security

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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.
Original languageEnglish
Title of host publicationEmerging Imaging and Sensing Technologies for Security and Defence V; and Advanced Manufacturing Technologies for Micro- and Nanosystems in Security and Defence III
EditorsGerald S. Buller, Richard C. Hollins, Robert A. Lamb, Martin Laurenzis, Andrea Camposeo, Maria Farsari, Luana Persano, Lynda E. Busse
Place of PublicationBellingham, Washington
Number of pages18
DOIs
Publication statusPublished - 21 Sep 2020
EventSPIE Security + Defence 2020 - Online
Duration: 21 Sep 202025 Sep 2020

Publication series

NameProceedings of SPIE
PublisherSPIE
Volume11540
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

ConferenceSPIE Security + Defence 2020
Period21/09/2025/09/20

Keywords

  • neuromorphic
  • deep learning
  • machine learning
  • SNN

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