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Large-Scale Neuromorphic Spiking Array Processors: A Quest to Mimic the Brain

Neuromorphic engineering (NE) encompasses a diverse range of approaches to information processing that are inspired by neurobiological systems, and this feature distinguishes neuromorphic systems from conventional computing systems. The brain has evolved over billions of years to solve difficult eng...

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Autores principales: Thakur, Chetan Singh, Molin, Jamal Lottier, Cauwenberghs, Gert, Indiveri, Giacomo, Kumar, Kundan, Qiao, Ning, Schemmel, Johannes, Wang, Runchun, Chicca, Elisabetta, Olson Hasler, Jennifer, Seo, Jae-sun, Yu, Shimeng, Cao, Yu, van Schaik, André, Etienne-Cummings, Ralph
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6287454/
https://www.ncbi.nlm.nih.gov/pubmed/30559644
http://dx.doi.org/10.3389/fnins.2018.00891
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author Thakur, Chetan Singh
Molin, Jamal Lottier
Cauwenberghs, Gert
Indiveri, Giacomo
Kumar, Kundan
Qiao, Ning
Schemmel, Johannes
Wang, Runchun
Chicca, Elisabetta
Olson Hasler, Jennifer
Seo, Jae-sun
Yu, Shimeng
Cao, Yu
van Schaik, André
Etienne-Cummings, Ralph
author_facet Thakur, Chetan Singh
Molin, Jamal Lottier
Cauwenberghs, Gert
Indiveri, Giacomo
Kumar, Kundan
Qiao, Ning
Schemmel, Johannes
Wang, Runchun
Chicca, Elisabetta
Olson Hasler, Jennifer
Seo, Jae-sun
Yu, Shimeng
Cao, Yu
van Schaik, André
Etienne-Cummings, Ralph
author_sort Thakur, Chetan Singh
collection PubMed
description Neuromorphic engineering (NE) encompasses a diverse range of approaches to information processing that are inspired by neurobiological systems, and this feature distinguishes neuromorphic systems from conventional computing systems. The brain has evolved over billions of years to solve difficult engineering problems by using efficient, parallel, low-power computation. The goal of NE is to design systems capable of brain-like computation. Numerous large-scale neuromorphic projects have emerged recently. This interdisciplinary field was listed among the top 10 technology breakthroughs of 2014 by the MIT Technology Review and among the top 10 emerging technologies of 2015 by the World Economic Forum. NE has two-way goals: one, a scientific goal to understand the computational properties of biological neural systems by using models implemented in integrated circuits (ICs); second, an engineering goal to exploit the known properties of biological systems to design and implement efficient devices for engineering applications. Building hardware neural emulators can be extremely useful for simulating large-scale neural models to explain how intelligent behavior arises in the brain. The principal advantages of neuromorphic emulators are that they are highly energy efficient, parallel and distributed, and require a small silicon area. Thus, compared to conventional CPUs, these neuromorphic emulators are beneficial in many engineering applications such as for the porting of deep learning algorithms for various recognitions tasks. In this review article, we describe some of the most significant neuromorphic spiking emulators, compare the different architectures and approaches used by them, illustrate their advantages and drawbacks, and highlight the capabilities that each can deliver to neural modelers. This article focuses on the discussion of large-scale emulators and is a continuation of a previous review of various neural and synapse circuits (Indiveri et al., 2011). We also explore applications where these emulators have been used and discuss some of their promising future applications.
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spelling pubmed-62874542018-12-17 Large-Scale Neuromorphic Spiking Array Processors: A Quest to Mimic the Brain Thakur, Chetan Singh Molin, Jamal Lottier Cauwenberghs, Gert Indiveri, Giacomo Kumar, Kundan Qiao, Ning Schemmel, Johannes Wang, Runchun Chicca, Elisabetta Olson Hasler, Jennifer Seo, Jae-sun Yu, Shimeng Cao, Yu van Schaik, André Etienne-Cummings, Ralph Front Neurosci Neuroscience Neuromorphic engineering (NE) encompasses a diverse range of approaches to information processing that are inspired by neurobiological systems, and this feature distinguishes neuromorphic systems from conventional computing systems. The brain has evolved over billions of years to solve difficult engineering problems by using efficient, parallel, low-power computation. The goal of NE is to design systems capable of brain-like computation. Numerous large-scale neuromorphic projects have emerged recently. This interdisciplinary field was listed among the top 10 technology breakthroughs of 2014 by the MIT Technology Review and among the top 10 emerging technologies of 2015 by the World Economic Forum. NE has two-way goals: one, a scientific goal to understand the computational properties of biological neural systems by using models implemented in integrated circuits (ICs); second, an engineering goal to exploit the known properties of biological systems to design and implement efficient devices for engineering applications. Building hardware neural emulators can be extremely useful for simulating large-scale neural models to explain how intelligent behavior arises in the brain. The principal advantages of neuromorphic emulators are that they are highly energy efficient, parallel and distributed, and require a small silicon area. Thus, compared to conventional CPUs, these neuromorphic emulators are beneficial in many engineering applications such as for the porting of deep learning algorithms for various recognitions tasks. In this review article, we describe some of the most significant neuromorphic spiking emulators, compare the different architectures and approaches used by them, illustrate their advantages and drawbacks, and highlight the capabilities that each can deliver to neural modelers. This article focuses on the discussion of large-scale emulators and is a continuation of a previous review of various neural and synapse circuits (Indiveri et al., 2011). We also explore applications where these emulators have been used and discuss some of their promising future applications. Frontiers Media S.A. 2018-12-03 /pmc/articles/PMC6287454/ /pubmed/30559644 http://dx.doi.org/10.3389/fnins.2018.00891 Text en Copyright © 2018 Thakur, Molin, Cauwenberghs, Indiveri, Kumar, Qiao, Schemmel, Wang, Chicca, Olson Hasler, Seo, Yu, Cao, van Schaik and Etienne-Cummings. http://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
Thakur, Chetan Singh
Molin, Jamal Lottier
Cauwenberghs, Gert
Indiveri, Giacomo
Kumar, Kundan
Qiao, Ning
Schemmel, Johannes
Wang, Runchun
Chicca, Elisabetta
Olson Hasler, Jennifer
Seo, Jae-sun
Yu, Shimeng
Cao, Yu
van Schaik, André
Etienne-Cummings, Ralph
Large-Scale Neuromorphic Spiking Array Processors: A Quest to Mimic the Brain
title Large-Scale Neuromorphic Spiking Array Processors: A Quest to Mimic the Brain
title_full Large-Scale Neuromorphic Spiking Array Processors: A Quest to Mimic the Brain
title_fullStr Large-Scale Neuromorphic Spiking Array Processors: A Quest to Mimic the Brain
title_full_unstemmed Large-Scale Neuromorphic Spiking Array Processors: A Quest to Mimic the Brain
title_short Large-Scale Neuromorphic Spiking Array Processors: A Quest to Mimic the Brain
title_sort large-scale neuromorphic spiking array processors: a quest to mimic the brain
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6287454/
https://www.ncbi.nlm.nih.gov/pubmed/30559644
http://dx.doi.org/10.3389/fnins.2018.00891
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