Gradient descent optimization for routing in multistage interconnection networks

Authors:
Mehran Ghaziasgar;Armin Tavakoli Naeini
Affiliations:
Department of Computer Science, Islamic Azad University, Majlesi Branch, Isfahan, Iran;McGill University, School of Computer Science
Venue:
IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part I
Year:
2011

Citing 4
Cited 0

Interconnection Networks: An Engineering Approach

Interconnection Networks: An Engineering Approach
Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models

Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models
Neural Network for Routing in a Directed and Weighted Graph

ISDA '08 Proceedings of the 2008 Eighth International Conference on Intelligent Systems Design and Applications - Volume 01
Letter: A binary Hopfield neural network with hysteresis for large crossbar packet-switches

Neurocomputing

Quantified Score

Hi-index	0.00

Visualization

Abstract

The design and development of interconnection networks is a critical issue in the field of communication switches, routers and multiprocessors. Here we consider the problem of routing in multistage interconnection networks (MIN). In our previously reported research we proposed a method to find the best path in a graph. In this paper, we use our algorithm which is based on neural network for routing in MINs. In this algorithm, we define a suitable energy function; the minimum of this function correspond to a valid route. By using gradient descent method, the energy is minimized at the convergence of neural network. Simulation results show that this method finds a valid path between source and destination and because neurons act in parallel, the performance is comparable with other approaches.