Please use this identifier to cite or link to this item:
|Title:||Efficient mapping algorithm of multilayer neural network on torus architecture||Authors:||Ayoubi, Rafic
Bayoumi, Magdy A.
|Affiliations:||Department of Computer Engineering||Keywords:||Feedforward neural nets
Neural net architecture
|Issue Date:||2003||Part of:||IEEE transactions on parallel and distributed systems||Volume:||14||Issue:||9||Start page:||932||End page:||943||Abstract:||
This paper presents a new efficient parallel implementation of neural networks on mesh-connected SIMD machines. A new algorithm to implement the recall and training phases of the multilayer perceptron network with back-error propagation is devised. The developed algorithm is much faster than other known algorithms of its class and comparable in speed to more complex architecture such as hypercube, without the added cost; it requires O(1) multiplications and O(log N) additions, whereas most others require O(N) multiplications and O(N) additions. The proposed algorithm maximizes parallelism by unfolding the ANN computation to its smallest computational primitives and processes these primitives in parallel.
|URI:||https://scholarhub.balamand.edu.lb/handle/uob/1936||Ezproxy URL:||Link to full text||Type:||Journal Article|
|Appears in Collections:||Department of Computer Engineering|
Show full item record
checked on Jun 5, 2023
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.