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IAS-Seminar "From ConvNets to cortex: an engineering perspective"

Anfang
23.11.2016 15:00 Uhr
Ende
23.11.2016 16:00 Uhr
Veranstaltungsort
Jülich Supercomputing Centre, Rotunde, Geb. 16.4, R. 301

Referent:
James Knight, Advanced Processor Technologies Research Group, University
of Manchester, UK
Abstract:
Convolutional neural networks (ConvNets) are inspired by the organization of the mammalian visual cortex and are currently the best performing solution to a large range of problems ranging from speech synthesis to image classification. In this talk, I will first compare the structure of ConvNets to that of the type of more realistic cortical models employed by theoretical neuroscientists. I will then discuss how the structures of these models affect efficiency when running them on different computer architectures: whereas GPUs are well suited to both training and performing inference using ConvNets, supercomputers or specialized neuromorphic hardware are currently the best means of running large-scale models of the cortex.
In the final section of this talk, I will discuss recent developments in bringing these two classes of models closer together. These developments include transferring ConvNets trained on GPUs to neuromorphic hardware – allowing inference to be performed at low power using spiking neurons.
Zeit:
Wednesday, 23 November 2016, 15:00
Ort:
Jülich Supercomputing Centre, Rotunda, building 16.4, room 301
Ankündigung als pdf-Datei:
 From ConvNets to cortex: an engineering perspective (PDF, 28 kB)

Alle Interessierten sind herzlich eingeladen.
Ansprechpartner: Dr. Jenia Jitsev, j.jitsev@fz-juelich.de


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