By Topic

Massively parallel architecture: application to neural net emulation and image reconstruction

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
D. Lattard ; IMAG-LGI, Grenoble, France ; B. Faure ; G. Mazare

The authors present two applications of a specific cellular architecture: emulation of the recall and learning for feedforward neural networks and parallel image reconstruction. This architecture is based on a bidimensional array of asynchronous processing elements, the cells, which can communicate between themselves by message transfers. Each cell includes a rotating routing part ensuring the message transportation through the array and a processing part dedicated to a particular application. The specificity of the processing part demands that it be redesigned for each application but leads to very fast computing and low complexity. This architecture can process algorithms not regular enough for SIMD machines. The cellular architecture is described, the feedforward neural network accelerator is introduced, the learning is discussed, and some time performances, evaluated by computer simulation, are given. The image reconstruction problem, its parallelization, some results of both functional and behavioral simulations, the realization of the circuit, and some test results are presented

Published in:

Application Specific Array Processors, 1990. Proceedings of the International Conference on

Date of Conference:

5-7 Sep 1990