• Additive manufacture of polymeric organometallic ferroelectric diodes (POMFeDs) for structural neuromorphic hardware

Browner, Davin, Sareh, Sina ORCID: https://orcid.org/0000-0002-9787-1798 and Anderson, Paul, 2023, Book Section, Additive manufacture of polymeric organometallic ferroelectric diodes (POMFeDs) for structural neuromorphic hardware In: Kudithipudi, Dhireesha, Frenkel, Charlotte, Cardwell, Suma and Aimone, James B., (eds.) NICE '23: Proceedings of the 2023 Annual Neuro-Inspired Computational Elements Conference. Association for Computing Machinery, New York, USA. ISBN 9781450399470

Abstract or Description:

Hardware design and implementation for online machine learning applications is complicated by a number of facets of conventional artificial neural networks (ANN), e.g. deep neural networks (DNNs), such as reliance on atemporal locality, offline learning using large datasets, potential difficulties in transfer from model to substrates, and issues with processing of noisy sensory data using energy-efficient and asynchronous information processing modalities. Analog or mixed-signal spiking neural networks (SNNs) have promise for lower power, temporally localised, and stimuli selective sensing and inference but are difficult fabricate at low cost. Investigation of beyond-CMOS alternative organic substrates may be worthwhile for development of unconventional neuromorphic hardware with pseudo-spiking dynamics for structural electronics integration in bio-signal processing and robotics. Here, polymeric organometallic ferroelectric diodes (POMFeDs) are introduced for development of printable ferroelectric in-sensor SNNs.

Official URL: https://dl.acm.org/doi/10.1145/3584954.3584998
Subjects: Other > Engineering > H100 General Engineering
Other > Engineering > H600 Electronic and Electrical Engineering > H670 Robotics and Cybernetics
Other > Technologies > J900 Others in Technology
Creative Arts and Design > W200 Design studies
Creative Arts and Design > W900 Others in Creative Arts and Design
School or Centre: Research & Innovation
School of Design
Identification Number or DOI: https://doi.org/10.1145/3584954.3584998
Uncontrolled Keywords: Event based spiking auditory sensors; ferroelectrics; additive manufacture; spiking neural networks
Date Deposited: 28 Apr 2023 10:43
Last Modified: 03 May 2023 10:55
URI: https://researchonline.rca.ac.uk/id/eprint/5358
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