Enabling Run-time Task Relocation on Reconfigurable Systems

Mignolet, J.-Y.; Nollet, V.; Coene, Paul; Verkest, D.; Vernalde, Serge; Lauwereins, Rudy

doi:10.1007/1-4020-3128-9_6

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“…The network outputs are compared with the actual outputs of the training example and an error signal is generated. In the backward pass, the synaptic weights are updated using the learning constant according to the effect they have in generating the incorrect outputs [12], [13].…”

Section: Background Of Neural Nteworkmentioning

confidence: 99%

A Framework for Embedded Hypercube Interconnection Networks: Based on Neural Network Approach

Ahmad¹,

Husain²,

Zilli³

2015

IJCA

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This paper is concerned with routing of data in an embedded hypercube interconnection using the approach based on neural net architecture. To present a framework of the interconnection network consist number of nodes and number of connections. In this paper we first show that n dimensional hypercube can be embedded in layer neural layer network such that for any node of hypercube, its neighboring nodes of other layer are evenly partition into layers where each layer shares a manipulating or resulting data of different layers. Under this embedding network to fixed target and varying data input to produce output of the two incidence matrix of kary n-cube network to embedded in architecture.

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Section: Background Of Neural Nteworkmentioning

confidence: 99%