Available

[Lee Giles]

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Reprint:USING RECURRENT NEURAL NETWORKS TO LEARN THE STRUCTURE
OF INTERCONNECTION NETWORKS

The following reprint is available via the University of Maryland
Department of Computer Science Technical Report archive:

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                  "Using Recurrent Neural Networks to 
           Learn the Structure of Interconnection Networks"

  UNIVERSITY OF MARYLAND TECHNICAL REPORT UMIACS-TR-94-20 AND CS-TR-3226
               
                G.W. Goudreau(a) and C.L. Giles(b,c)

           goudreau at cs.ucf.edu, giles at research.nj.nec.com

(a) Department of Computer Science, U. of Central Florida, Orlando, FL 32816
(b) NEC Research Inst.,4 Independence Way, Princeton, NJ 08540
(c) Inst. for Advanced Computer Studies, U. of Maryland, College Park, MD 20742  


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.

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--                                 
C. Lee Giles / NEC Research Institute / 4 Independence Way
Princeton, NJ 08540 / 609-951-2642 / Fax 2482
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