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In-Materio Extreme Learning Machines

Jones, Benedict A.H.; Al Moubayed, Noura; Zeze, Dagou A.; Groves, Chris

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Authors

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Benedict Jones benedict.jones@durham.ac.uk
PGR Student Doctor of Philosophy



Contributors

Günter Rudolph
Editor

Anna V. Kononova
Editor

Hernán Aguirre
Editor

Pascal Kerschke
Editor

Gabriela Ochoa
Editor

Tea Tušar
Editor

Abstract

Nanomaterial networks have been presented as a building block for unconventional in-Materio processors. Evolution in-Materio (EiM) has previously presented a way to congure and exploit physical materials for computation, but their ability to scale as datasets get larger and more complex remains unclear. Extreme Learning Machines (ELMs) seek to exploit a randomly initialised single layer feed forward neural network by training the output layer only. An analogy for a physical ELM is produced by exploiting nanomaterial networks as material neurons within the hidden layer. Circuit simulations are used to eciently investigate diode-resistor networks which act as our material neurons. These in-Materio ELMs (iM-ELMs) outperform common classication methods and traditional articial ELMs of a similar hidden layer size. For iM-ELMs using the same number of hidden layer neurons, leveraging larger more complex material neuron topologies (with more nodes/electrodes) leads to better performance, showing that these larger materials have a better capability to process data. Finally, iM-ELMs using virtual material neurons, where a single material is re-used as several virtual neurons, were found to achieve comparable results to iM-ELMs which exploited several dierent materials. However, while these Virtual iM-ELMs provide signicant exibility, they sacrice the highly parallelised nature of physically implemented iM-ELMs.

Citation

Jones, B. A., Al Moubayed, N., Zeze, D. A., & Groves, C. (2022). In-Materio Extreme Learning Machines. In G. Rudolph, A. V. Kononova, H. Aguirre, P. Kerschke, G. Ochoa, & T. Tušar (Eds.), Parallel Problem Solving from Nature – PPSN XVII (505-519). Springer Verlag. https://doi.org/10.1007/978-3-031-14714-2_35

Acceptance Date Jun 6, 2022
Online Publication Date Aug 14, 2022
Publication Date 2022
Deposit Date Aug 3, 2022
Publicly Available Date Aug 15, 2023
Publisher Springer Verlag
Pages 505-519
Series Title Lecture Notes in Computer Science
Series Number 13398
Book Title Parallel Problem Solving from Nature – PPSN XVII
ISBN 978-3-031-14713-5
DOI https://doi.org/10.1007/978-3-031-14714-2_35
Public URL https://durham-repository.worktribe.com/output/1649976

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