Systolic designs for state space models: Kalman filtering and neural network

In this paper, 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 pipelin 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 network (ANN), which has recently received increasing attention from AI and parallel processing research community.