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Proceedings Paper

Systolic Architectures For Hidden Markov Models
Author(s): J. N. Hwang; J. A. Vlontzos; S. Y. Kung
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Paper Abstract

This paper proposes an unidirectional ring systolic architecture for implementing the hidden Markov models (HMMs). This array architecture maximizes the strength of VLSI in terms of intensive and pipelined computing and yet circumvents the limitation on communication. Both the scoring and learning phases of an HMM are formulated as a consecutive matrix-vector multiplication problem, which can be executed in a fully pipelined fashion (100% utilization effi-ciency) by using an unidirectional ring systolic architecture. By appropriately scheduling the algorithm, which combines both the operations of the backward evaluation procedure and reestimation algorithm at the same time, we can use this systolic HMM in a most efficient manner. The systolic HMM can also be easily adapted to the left-to-right HMM by using bidirectional semi-global links with significant time saving. This architecture can also incorporate the scaling scheme with little extra effort in the computations of forward and backward evaluation variables to prevent the frequently encountered mathematical undertow problems. We also discuss a possible implementation of this proposed architecture using Inmos transputer (T-800) as the building block.

Paper Details

Date Published: 25 October 1988
PDF: 8 pages
Proc. SPIE 1001, Visual Communications and Image Processing '88: Third in a Series, (25 October 1988); doi: 10.1117/12.968970
Show Author Affiliations
J. N. Hwang, University of Southern California (United States)
J. A. Vlontzos, University of Southern California (United States)
S. Y. Kung, Princeton University (United States)

Published in SPIE Proceedings Vol. 1001:
Visual Communications and Image Processing '88: Third in a Series
T. Russell Hsing, Editor(s)

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