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dc.creatorFarabet, Clémentes
dc.creatorPaz Vicente, Rafaeles
dc.creatorPerez Carrasco, Josées
dc.creatorZamarreño Ramos, Carloses
dc.creatorLinares Barranco, Alejandroes
dc.creatorLeCun, Yannes
dc.creatorCulurciello, Eugenioes
dc.creatorSerrano Gotarredona, María Teresaes
dc.creatorLinares Barranco, Bernabées
dc.date.accessioned2018-03-07T18:56:53Z
dc.date.available2018-03-07T18:56:53Z
dc.date.issued2012
dc.identifier.citationFarabet, C., Paz Vicente, R., Perez Carrasco, J., Zamarreño Ramos, C., Linares Barranco, A., LeCun, Y.,...,Linares Barranco, B. (2012). Comparison between frame-constrained fix-pixel-value and frame-free spiking-dynamic-pixel convNets for visual processing. Frontiers in Neuroscience, 6 (32), 1-12.
dc.identifier.issn1662-4548 (impreso)es
dc.identifier.issn1662-453X (electrónico)es
dc.identifier.urihttps://hdl.handle.net/11441/70874
dc.description.abstractMost scene segmentation and categorization architectures for the extraction of features in images and patches make exhaustive use of 2D convolution operations for template matching, template search, and denoising. Convolutional Neural Networks (ConvNets) are one example of such architectures that can implement general-purpose bio-inspired vision systems. In standard digital computers 2D convolutions are usually expensive in terms of resource consumption and impose severe limitations for efficient real-time applications. Nevertheless, neuro-cortex inspired solutions, like dedicated Frame-Based or Frame-Free Spiking ConvNet Convolution Processors, are advancing real-time visual processing. These two approaches share the neural inspiration, but each of them solves the problem in different ways. Frame-Based ConvNets process frame by frame video information in a very robust and fast way that requires to use and share the available hardware resources (such as: multipliers, adders). Hardware resources are fixed- and time-multiplexed by fetching data in and out. Thus memory bandwidth and size is important for good performance. On the other hand, spike-based convolution processors are a frame-free alternative that is able to perform convolution of a spike-based source of visual information with very low latency, which makes ideal for very high-speed applications. However, hardware resources need to be available all the time and cannot be time-multiplexed. Thus, hardware should be modular, reconfigurable, and expansible. Hardware implementations in both VLSI custom integrated circuits (digital and analog) and FPGA have been already used to demonstrate the performance of these systems. In this paper we present a comparison study of these two neuro-inspired solutions. A brief description of both systems is presented and also discussions about their differences, pros and cons.es
dc.formatapplication/pdfes
dc.language.isoenges
dc.publisherFrontiers Mediaes
dc.relation.ispartofFrontiers in Neuroscience, 6 (32), 1-12.
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectConvolutional neural networkes
dc.subjectAddress-event representationes
dc.subjectSpike-based convolutionses
dc.subjectImage convolutionses
dc.subjectFrame-free visiones
dc.subjectFPGAes
dc.subjectVHDLes
dc.titleComparison between frame-constrained fix-pixel-value and frame-free spiking-dynamic-pixel convNets for visual processinges
dc.typeinfo:eu-repo/semantics/articlees
dcterms.identifierhttps://ror.org/03yxnpp24
dc.type.versioninfo:eu-repo/semantics/publishedVersiones
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses
dc.contributor.affiliationUniversidad de Sevilla. Departamento de Arquitectura y Tecnología de Computadoreses
dc.relation.publisherversionhttp://dx.doi.org/10.3389/fnins.2012.00032es
dc.identifier.doi10.3389/fnins.2012.00032es
idus.format.extent12 p.es
dc.journaltitleFrontiers in Neurosciencees
dc.publication.volumen6es
dc.publication.issue32es
dc.publication.initialPage1es
dc.publication.endPage12es

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