Cargando…

Neuromorphic Engineering Needs Closed-Loop Benchmarks

Neuromorphic engineering aims to build (autonomous) systems by mimicking biological systems. It is motivated by the observation that biological organisms—from algae to primates—excel in sensing their environment, reacting promptly to their perils and opportunities. Furthermore, they do so more resil...

Descripción completa

Detalles Bibliográficos
Autores principales: Milde, Moritz B., Afshar, Saeed, Xu, Ying, Marcireau, Alexandre, Joubert, Damien, Ramesh, Bharath, Bethi, Yeshwanth, Ralph, Nicholas O., El Arja, Sami, Dennler, Nik, van Schaik, André, Cohen, Gregory
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8884247/
https://www.ncbi.nlm.nih.gov/pubmed/35237122
http://dx.doi.org/10.3389/fnins.2022.813555
_version_ 1784660109595508736
author Milde, Moritz B.
Afshar, Saeed
Xu, Ying
Marcireau, Alexandre
Joubert, Damien
Ramesh, Bharath
Bethi, Yeshwanth
Ralph, Nicholas O.
El Arja, Sami
Dennler, Nik
van Schaik, André
Cohen, Gregory
author_facet Milde, Moritz B.
Afshar, Saeed
Xu, Ying
Marcireau, Alexandre
Joubert, Damien
Ramesh, Bharath
Bethi, Yeshwanth
Ralph, Nicholas O.
El Arja, Sami
Dennler, Nik
van Schaik, André
Cohen, Gregory
author_sort Milde, Moritz B.
collection PubMed
description Neuromorphic engineering aims to build (autonomous) systems by mimicking biological systems. It is motivated by the observation that biological organisms—from algae to primates—excel in sensing their environment, reacting promptly to their perils and opportunities. Furthermore, they do so more resiliently than our most advanced machines, at a fraction of the power consumption. It follows that the performance of neuromorphic systems should be evaluated in terms of real-time operation, power consumption, and resiliency to real-world perturbations and noise using task-relevant evaluation metrics. Yet, following in the footsteps of conventional machine learning, most neuromorphic benchmarks rely on recorded datasets that foster sensing accuracy as the primary measure for performance. Sensing accuracy is but an arbitrary proxy for the actual system's goal—taking a good decision in a timely manner. Moreover, static datasets hinder our ability to study and compare closed-loop sensing and control strategies that are central to survival for biological organisms. This article makes the case for a renewed focus on closed-loop benchmarks involving real-world tasks. Such benchmarks will be crucial in developing and progressing neuromorphic Intelligence. The shift towards dynamic real-world benchmarking tasks should usher in richer, more resilient, and robust artificially intelligent systems in the future.
format Online
Article
Text
id pubmed-8884247
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-88842472022-03-01 Neuromorphic Engineering Needs Closed-Loop Benchmarks Milde, Moritz B. Afshar, Saeed Xu, Ying Marcireau, Alexandre Joubert, Damien Ramesh, Bharath Bethi, Yeshwanth Ralph, Nicholas O. El Arja, Sami Dennler, Nik van Schaik, André Cohen, Gregory Front Neurosci Neuroscience Neuromorphic engineering aims to build (autonomous) systems by mimicking biological systems. It is motivated by the observation that biological organisms—from algae to primates—excel in sensing their environment, reacting promptly to their perils and opportunities. Furthermore, they do so more resiliently than our most advanced machines, at a fraction of the power consumption. It follows that the performance of neuromorphic systems should be evaluated in terms of real-time operation, power consumption, and resiliency to real-world perturbations and noise using task-relevant evaluation metrics. Yet, following in the footsteps of conventional machine learning, most neuromorphic benchmarks rely on recorded datasets that foster sensing accuracy as the primary measure for performance. Sensing accuracy is but an arbitrary proxy for the actual system's goal—taking a good decision in a timely manner. Moreover, static datasets hinder our ability to study and compare closed-loop sensing and control strategies that are central to survival for biological organisms. This article makes the case for a renewed focus on closed-loop benchmarks involving real-world tasks. Such benchmarks will be crucial in developing and progressing neuromorphic Intelligence. The shift towards dynamic real-world benchmarking tasks should usher in richer, more resilient, and robust artificially intelligent systems in the future. Frontiers Media S.A. 2022-02-14 /pmc/articles/PMC8884247/ /pubmed/35237122 http://dx.doi.org/10.3389/fnins.2022.813555 Text en Copyright © 2022 Milde, Afshar, Xu, Marcireau, Joubert, Ramesh, Bethi, Ralph, El Arja, Dennler, van Schaik and Cohen. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Milde, Moritz B.
Afshar, Saeed
Xu, Ying
Marcireau, Alexandre
Joubert, Damien
Ramesh, Bharath
Bethi, Yeshwanth
Ralph, Nicholas O.
El Arja, Sami
Dennler, Nik
van Schaik, André
Cohen, Gregory
Neuromorphic Engineering Needs Closed-Loop Benchmarks
title Neuromorphic Engineering Needs Closed-Loop Benchmarks
title_full Neuromorphic Engineering Needs Closed-Loop Benchmarks
title_fullStr Neuromorphic Engineering Needs Closed-Loop Benchmarks
title_full_unstemmed Neuromorphic Engineering Needs Closed-Loop Benchmarks
title_short Neuromorphic Engineering Needs Closed-Loop Benchmarks
title_sort neuromorphic engineering needs closed-loop benchmarks
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8884247/
https://www.ncbi.nlm.nih.gov/pubmed/35237122
http://dx.doi.org/10.3389/fnins.2022.813555
work_keys_str_mv AT mildemoritzb neuromorphicengineeringneedsclosedloopbenchmarks
AT afsharsaeed neuromorphicengineeringneedsclosedloopbenchmarks
AT xuying neuromorphicengineeringneedsclosedloopbenchmarks
AT marcireaualexandre neuromorphicengineeringneedsclosedloopbenchmarks
AT joubertdamien neuromorphicengineeringneedsclosedloopbenchmarks
AT rameshbharath neuromorphicengineeringneedsclosedloopbenchmarks
AT bethiyeshwanth neuromorphicengineeringneedsclosedloopbenchmarks
AT ralphnicholaso neuromorphicengineeringneedsclosedloopbenchmarks
AT elarjasami neuromorphicengineeringneedsclosedloopbenchmarks
AT dennlernik neuromorphicengineeringneedsclosedloopbenchmarks
AT vanschaikandre neuromorphicengineeringneedsclosedloopbenchmarks
AT cohengregory neuromorphicengineeringneedsclosedloopbenchmarks