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A communication architecture tailored for analog VLSI artificial neural networks: intrinsic performance and limitations

Authors:

A. Mortara,

E. A. VittozAuthors Info & Claims

IEEE Transactions on Neural Networks, Volume 5, Issue 3

Pages 459 - 466

https://doi.org/10.1109/72.286916

Published: 01 May 1994 Publication History

Abstract

An architecture for interchip communication among analog VLSI neural networks is proposed. Activity is encoded in a neuron's pulse emission frequency. Information is transmitted through the non-arbitered, asynchronous access of pulses to a common bus. The impact of collisions when the bus is accessed by more than one user is investigated. The information-carrying capability is assessed and the trade-off between accuracy of the transmitted information and attainable dynamic range is brought out in terms of simple global parameters that characterize the application. It is found that the proposed architecture is well suited for the kind of communication requirements associated to neural computation systems. A coding scheme aimed at pushing the system towards its theoretical performance is also presented and evaluated

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Walter FRöhrbein FKnoll A(2015)Neuromorphic implementations of neurobiological learning algorithms for spiking neural networksNeural Networks10.1016/j.neunet.2015.07.00472:C(152-167)Online publication date: 1-Dec-2015
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Misra JSaha I(2010)Artificial neural networks in hardwareNeurocomputing10.1016/j.neucom.2010.03.02174:1-3(239-255)Online publication date: 1-Dec-2010
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A communication architecture tailored for analog VLSI artificial neural networks: intrinsic performance and limitations

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cover image IEEE Transactions on Neural Networks

IEEE Transactions on Neural Networks Volume 5, Issue 3

May 1994

189 pages

ISSN:1045-9227

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IEEE Press

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Published: 01 May 1994

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Guo SKang ZWang LLi SXu WCheng KYang H(2020)HashHeat: An O(C) Complexity Hashing-Based Filter for Dynamic Vision SensorProceedings of the 25th Asia and South Pacific Design Automation Conference10.1109/ASP-DAC47756.2020.9045268(452-457)Online publication date: 17-Jan-2020
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Walter FRöhrbein FKnoll A(2015)Neuromorphic implementations of neurobiological learning algorithms for spiking neural networksNeural Networks10.1016/j.neunet.2015.07.00472:C(152-167)Online publication date: 1-Dec-2015
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Misra JSaha I(2010)Artificial neural networks in hardwareNeurocomputing10.1016/j.neucom.2010.03.02174:1-3(239-255)Online publication date: 1-Dec-2010
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Teixeira TCulurciello EPark JLymberopoulos DBarton-Sweeney ASavvides AStankovic JGibbons PWicker SParadiso J(2006)Address-event imagers for sensor networksProceedings of the 5th international conference on Information processing in sensor networks10.1145/1127777.1127847(458-466)Online publication date: 19-Apr-2006
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Elliott TKramer J(2002)Coupling an a VLSI Neuromorphic vision chip to a neurotrophic model of synaptic plasticityNeural Computation10.1162/0899766026029325614:10(2353-2370)Online publication date: 1-Oct-2002
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