Using Recurrent Neural Networks to Learn the Structure of Interconnection Networks

Loading...
Thumbnail Image

Files

CS-TR-3226.ps (220.89 KB)
No. of downloads: 280
CS-TR-3226.pdf (218.21 KB)
No. of downloads: 740

Publication or External Link

Date

1998-10-15

Advisor

Citation

DRUM DOI

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. (Also cross-referenced as UMIACS-TR-94-20.)

Notes

Rights