A systematic mapping methodology is introduced for deriving systolic and wavefront arrays from regular computational algorithms. 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 pipeline rates, etc. Based on this methodology, systolic array designs are developed for two applications of adaptive state-space models. One is for the Kalman filtering algorithm which is popular in many digital signal processing applications, and the other is the Hopfield model for artificial neural networks (ANN), which has recently received increasing attention from the artificial-intelligence (AI) and parallel-processing research community.
|Number of pages
|Proceedings of the IEEE Conference on Decision and Control
|Published - Dec 1 1987
All Science Journal Classification (ASJC) codes
- Control and Systems Engineering
- Modeling and Simulation
- Control and Optimization