Title

Using Recurrent Neural Networks To Learn The Structure Of Interconnection Networks

Authors

Authors

M. W. Goudreau;C. L. Giles

Comments

Authors: contact us about adding a copy of your work at STARS@ucf.edu

Abbreviated Journal Title

Neural Netw.

Keywords

INTERCONNECTION NETWORKS; RECURRENT NETWORKS; REAL-TIME TRAINING; KNOWLEDGE EXTRACTION; SEQUENTIAL MACHINES; FINITE-STATE AUTOMATA; ARCHITECTURE; COMPLEXITY; INDUCTION; Computer Science, Artificial Intelligence

Abstract

A modified Recurrent Neural Network (RNN) is used to learn a Self-Routing Interconnection Network (SRIN) from a set of routing examples. The RNN is modified so that it has several distinct initial states. This is equivalent to a single RNN learning multiple different synchronous sequential machines. We define such a sequential machine structure as augmented and show that a SRIN is essentially an Augmented Synchronous Sequential Machine (ASSM). As an example, we learn a small six-switch SRIN. After training we extract the network's internal representation of the ASSM and corresponding SRIN.

Journal Title

Neural Networks

Volume

8

Issue/Number

5

Publication Date

1-1-1995

Document Type

Article

Language

English

First Page

793

Last Page

804

WOS Identifier

WOS:A1995TB35600011

ISSN

0893-6080

Share

COinS