reprint available

[Lee Giles]

The following conference paper published in the 2nd International IEEE
Conference on "Massively Parallely Processing Using Optical
Interconnections," October, 1995 is now available via the NEC Research
Institute archive:

____________________________________________________________________________________

          "Predictive Control of Opto-Electronic Reconfigurable 
             Interconnection Networks Using Neural Networks"

         Majd F. Sakr[1,2], Steven P. Levitan[2], C. Lee Giles[1,3], 
         Bill G. Horne[1], Marco Maggini[4], Donald M. Chiarulli[5] 

     [1] NEC Research Institute, 4 Independence Way, Princeton, NJ  08540
 [2] Electrical Engineering Department, U. of Pittsburgh, Pittsburgh, PA 15261
            [3] UMIACS, U. of Maryland, College Park, MD 20742
[4] Universit` di Firenze, Dipartimento di Sistemi e Informatica, 
	50139 Firenze, Italy   
    [5] Computer Science Department, U. of Pittsburgh, Pittsburgh, PA 15260
                                      

                                  Abstract

Opto-electronic reconfigurable interconnection networks are limited by
significant control latency when used in large multiprocessor systems. This
latency is the time required to analyze the current traffic and reconfigure
the network to establish the required paths. The goal of latency hiding is
to minimize the effect of this control overhead. In this paper, we
introduce a technique that performs latency hiding by learning the patterns
of communication traffic and using that information to anticipate the need
for communication paths. Hence, the network provides the required
communication paths before a request for a path is made. In this study, the
communication patterns (memory accesses) of a parallel program are used as
input to a time delay neural network (TDNN) to perform on-line training and
prediction. These predicted communication patterns are used by the
interconnection network controller that provides routes for the memory
requests.  Based on our experiments, the neural network was able to learn
highly repetitive communication patterns, and was thus able to predict the
allocation of communication paths, resulting in a reduction of
communication latency.

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http://www.neci.nj.nec.com/homepages/giles.html
ftp://external.nj.nec.com/pub/giles/papers/MPPOI.95.ps.Z

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--                                 
C. Lee Giles / Computer Sciences / NEC Research Institute / 
4 Independence Way / Princeton, NJ 08540, USA / 609-951-2642 / Fax 2482
http://www.neci.nj.nec.com/homepages/giles.html
==