The Saxpy Matrix-1: A General-Purpose Systolic Computer

Abstract

There has been no reduction in the demand for higher performance in scientific computation. This is especially true in digital signal processing because new algorithms for that application require much more computation than do conventional techniques. The increasing volumes of data from seismic surveys, sonar and radar systems, and imaging systems need to be processed; these applications are important sources of demand for more throughput. The analysis of data that are generated by such new imaging technologies as magnetic resonance imaging creates additional demand. In signal processing and scientific computing, parallelism is pervasive. While the granularity of the parallelism may vary, the opportunity to perform many calculations in parallel is characteristic of it. Harnessing this inherent, abundant parallelism through new computer architectures is now one of the major thrusts in computer technology.