Fault-Tolerant Recursive Least-Squares Computations on a Mesh-Connected Parallel Processor

Abstract

Recursive least squares (RLS) is a popular iterative method used for the modeling of systems while in operation. RLS provides an estimate for unknown parameters of a system based on some known parameters and inputs and outputs of that system. This technique is used frequently in digital signal processing and control applications, where it is not possible to completely determine the current state of the system. The RLS procedure incurs intensive computations in every iteration of the algorithm. To implement RLS in situ at a reasonable sampling rate, the complexity of the system's model must be reduced, or the available computing power must be increased. This paper examines methods for increasing the computing power by implementing RLS algorithms on a parallel processing platform. While there has been a large body of research on using parallel processors for the computation of adaptive algorithms, little of this research has examined fault tolerant aspects. As fault tolerance is a critical aspect of any real-time system, this work will examine some factors that should be considered when implementing a real-time adaptive algorithm on a parallel processor system.