Brian J. d'Auriol, Ph.D.

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Brian J. d'Auriol, Virendra C. Bhavsar and Lev Goldfarb, ``Systolic Array Implementations for Reconfigurable Learning Machines on Transputers'', Proc. of Supercomputing Symposium '91, Fredericton, N.B., Canada, June 3-5, 1991, V.C. Bhavsar and U.G. Gujar (Eds.), University of New Brunswick Press, Fredericton, N.B., pp. 105-119, June 1991.

A systolic array implementation for a Reconfigurable Learning Machine (RLM) in the Occam 2 language (executed on transputers) is proposed. The theoretical model, Evolving Transformation Systems, which forms the basis for RLM, is outlined. This is a general adaptive model unifying existing pattern learning models. Central to our implementation of RLM is both the computation of a general Weighted Levenshtein Distance (WLD) using a known systolic array algorithm and substring matching. We propose a new algorithm for substring matching based on this systolic array as well as a general method to represent the properties of systolic arrays, in particular the two dimensional, hexagonal systolic array in the Occam 2 language. This method is defined as Systolic Array Mapping. Some initial timings are also included.

Last Updated: July 28, 2007