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Neuromorphic computing gets ready for the (really) big time

Published: 01 June 2014 Publication History

Abstract

A technology inspired by biological principles but 'steamrolled for decades' prepares to take off as Moore's Law approaches its long-anticipated end.

References

[1]
"A New Era of Computing Requires a New Way to Program Computers," Dharmendra S. Modha, http://bit.ly/1i2rxqh
[2]
"Low-Power Chips to Model a Billion Neurons," Steve Furber, IEEE Spectrum, August 2012, http://bit.ly/1fuotGv
[3]
"Cognitive Computing," Dharmendra S. Modha, Rajagopal Ananthanarayanan, Steven K. Esser, Anthony Ndirango, Anthony J. Sherbondy, Raghavendra Singh, CACM, August 2011, http://bit.ly/1i2rtqz
[4]
"A Computer that Works like the Human Brain," a TEDx talk with bioengineer Kwabena Boahen, http://bit.ly/1gOY0UM

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  • (2023)Experimental Study of a Prototype of a Superconducting Sigma Neuron for Adiabatic Neural NetworksŽurnal èksperimentalʹnoj i teoretičeskoj fiziki10.31857/S0044451023120143164:6(1008-1021)Online publication date: 15-Dec-2023
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  1. Neuromorphic computing gets ready for the (really) big time

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      Mohammed Ziaur Rahman

      Some global initiatives to build a biologically inspired computer are highlighted in this article. The term “neuromorphic” is used to refer to biologically inspired computing as a whole, and specifically to analog circuits for neural network implementations. The Defense Advanced Research Projects Agency (DARPA)-funded Systems of Neuromorphic Adaptive Plastic Scalable Electronics (SyNAPSE) and the Neurogrid project at Stanford University, both US initiatives, are highlighted before referring to a commercial initiative by Qualcomm. The research goals vary from implementation of a few hundred to billions of neurons to the development of analog neuromorphic systems. Apart from physical implementation, software remains the most critical component for the success of these systems. The task is to rethink programming and algorithms for fitting into these systems. Therefore, researchers are collaborating to develop a neural compiler that will enable developers to move their proven algorithms to the new architecture. From the industry side, Qualcomm's neural core processor Zeroth is reported. It aims “to bring the sort of intelligence that people usually associate with the cloud down to the handset.” Two projects from the European Union's billion-euro Human Brain Project (HBP) are highlighted. The first one is SpiNNaker (Spiking Neural Network Architecture) from the University of Manchester. It is completely digital and connects ARM cores in a massively parallel network whose ultimate aim is to connect a million cores for brain research purposes. The second one is a neuromorphic project from the University of Heidelberg. It is focused on using mixed-signal neurons “to reduce the need to drive off-chip interconnections.” Can these projects bring more successes than their predecessors, “the artificial neural networks of a quarter century ago”__?__ This article both raises the question and gives an indirect answer. Online Computing Reviews Service

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      Published In

      cover image Communications of the ACM
      Communications of the ACM  Volume 57, Issue 6
      June 2014
      103 pages
      ISSN:0001-0782
      EISSN:1557-7317
      DOI:10.1145/2602695
      • Editor:
      • Moshe Y. Vardi
      Issue’s Table of Contents
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 01 June 2014
      Published in CACM Volume 57, Issue 6

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      • (2024)A comparative study on ReLU Implementation using TMDFETsPhysica Scripta10.1088/1402-4896/ad508399:7(075923)Online publication date: 6-Jun-2024
      • (2023)Intelligent Computing: The Latest Advances, Challenges, and FutureIntelligent Computing10.34133/icomputing.00062Online publication date: 30-Jan-2023
      • (2023)Experimental Study of a Prototype of a Superconducting Sigma Neuron for Adiabatic Neural NetworksŽurnal èksperimentalʹnoj i teoretičeskoj fiziki10.31857/S0044451023120143164:6(1008-1021)Online publication date: 15-Dec-2023
      • (2023)A Tandem Learning Rule for Effective Training and Rapid Inference of Deep Spiking Neural NetworksIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2021.309572434:1(446-460)Online publication date: Jan-2023
      • (2023)A Machine Learning Approach to Support Neuromorphic Device Design and Microfabrication2023 International Conference on Machine Learning and Applications (ICMLA)10.1109/ICMLA58977.2023.00246(1627-1634)Online publication date: 15-Dec-2023
      • (2023)Quantum and Classical Correlations in Open Quantum Spin Lattices via Truncated-Cumulant TrajectoriesPRX Quantum10.1103/PRXQuantum.4.0303044:3Online publication date: 12-Jul-2023
      • (2023) Emulation of Pavlovian conditioning and pattern recognition through fully connected neural networks using Holmium oxide (Ho 2 O 3 ) based synaptic RRAM device Nanotechnology10.1088/1361-6528/ad0bd135:7(075701)Online publication date: 28-Nov-2023
      • (2023)Nanoscale electronic synapses for neuromorphic computingIntelligent Nanotechnology10.1016/B978-0-323-85796-3.00007-X(189-218)Online publication date: 2023
      • (2023)A generalized Caputo-type fractional-order neuron model under the electromagnetic fieldInternational Journal of Dynamics and Control10.1007/s40435-023-01134-411:5(2179-2192)Online publication date: 2-Mar-2023
      • (2023)Unconventional computing based on magnetic tunnel junctionApplied Physics A10.1007/s00339-022-06365-4129:4Online publication date: 3-Mar-2023
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