Systolic algorithms and architectures for high-throughput processing applications

In this paper we survey a number of linear algebra algorithms that are used in many modern real-time high-throughput applications. We focus in particular on algorithms that are used in applications as diverse as adaptive filtering/beamforming, communications, signal/array processing, control, etc. We first consider least-squares estimation, QR decomposition, singular value decomposition, recursive least-squares estimation, and Kalman filtering algorithms. In particular, the QR decomposition which is used in least-squares solutions of linear system of equations, also forms one component of more complex algorithms such as Kalman filtering and the singular value decomposition. Systolic arrays, due to their modularity and regularity, are perfect matches for all the algorithms considered in this paper. All these problems are defined and described alongside the systolic algorithms and the architectures that implement them.