Using Recurrent Neural Networks to Learn the Structure of Interconnection Networks
| dc.contributor.author | Goudreau, Mark W. | en_US |
| dc.contributor.author | Giles, C. Lee | en_US |
| dc.date.accessioned | 2004-05-31T22:25:10Z | |
| dc.date.available | 2004-05-31T22:25:10Z | |
| dc.date.created | 1994-02 | en_US |
| dc.date.issued | 1998-10-15 | en_US |
| dc.description.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.) | en_US |
| dc.format.extent | 226192 bytes | |
| dc.format.mimetype | application/postscript | |
| dc.identifier.uri | http://hdl.handle.net/1903/617 | |
| dc.language.iso | en_US | |
| dc.relation.isAvailableAt | Digital Repository at the University of Maryland | en_US |
| dc.relation.isAvailableAt | University of Maryland (College Park, Md.) | en_US |
| dc.relation.isAvailableAt | Tech Reports in Computer Science and Engineering | en_US |
| dc.relation.isAvailableAt | UMIACS Technical Reports | en_US |
| dc.relation.ispartofseries | UM Computer Science Department; CS-TR-3226 | en_US |
| dc.relation.ispartofseries | UMIACS; UMIACS-TR-94-20. | en_US |
| dc.title | Using Recurrent Neural Networks to Learn the Structure of Interconnection Networks | en_US |
| dc.type | Technical Report | en_US |