Mapping of Neural Network Models onto Systolic Arrays

Authors: Mahapatra S.¹; Mahapatra R.N.²

Source: Journal of Parallel and Distributed Computing, Volume 60, Number 6, June 2000 , pp. 677-689(13)

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Abstract:

This paper presents a mapping scheme for the proposed implementation of neural network models on systolic arrays. The mapping technique is illustrated on the multilayer perceptron with back-propagation learning. Dependency graphs have been given that represent the operations in the execution phases of the neural network model and later suitable algorithms are presented to realize the operations in a linear bidirectional systolic array. The speedup metric has been used to evaluate the performance of the proposed implementation. Copyright 2000 Academic Press.

Keywords: neural net models; dependency graph; mapping scheme; bidirectional systolic array; back-propagation algorithm

Language: English

Document Type: Research article

Affiliations: 1: Department of Computer Science Engineering and Applications, Regional Engineering College, Rourkela, Orissa, 769 008, India 2: Department of Computer Science, Texas A&M University, College Station, Texas, 77843-3112

Publication date: 2000-06-01