Abstract
In this paper, a systematic mapping methodology is introduced for deriving systolic and wavefront arrays from regular computational algorithms [10]. It consists of three stages of mapping design: (data) dependence graph (DG) design, signal flow graph (SFG) design, and array processor design. This methodology allows systolic design with many desirable properties, such as local communication and fastest pipelining rates, etc. Based on this methodology, we shall develop systolic array designs for two important applications of adaptive state-space models. One is for the Kalman filtering algorithm which is popular in many digital signal processing applications. The other one is the Hopfield model for artificial neural networks (ANN), which has recently received increasing attention from AI and parallel processing research community.
This paper was also presented at the 26th IEEE Conf. on Decision and Control, Los Angeles, CA, Dec. 9–11, 1987 and appeared in Proc. 26th IEEE Conf. Decision and Control pp. 1461–1467, 1987. ©1987 IEEE.
This research was supported in part by the National Science Foundation under Grant ECS82-13358, by the Semiconductor Research Corporation under USC SRC program, and by the Innovative Science and Technology Office of the Strategic Defense Initiative Organization and was administered through the Office of Naval Research under Contract No. N00014-85-K-0469 and N00014-85-K-0599.
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© 1988 Plenum Press, New York
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Kung, SY., Huang, J.N. (1988). Systolic Designs for State Space Models: Kalman Filtering and Neural Networks. In: Tewksbury, S.K., Dickinson, B.W., Schwartz, S.C. (eds) Concurrent Computations. Springer, Boston, MA. https://doi.org/10.1007/978-1-4684-5511-3_31
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DOI: https://doi.org/10.1007/978-1-4684-5511-3_31
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