Mapping Signal Processing Algorithms to Architectures - Course Introduction

Digital Signal Processing typically involves repetitive computations being performed on streams of input data, subject to constraints such as sampling rate or desired throughput. Often such systems need to be implemented under tight constraints on factors such as timing, resources, power or cost. When they are used in embedded systems, it is often worth the effort to design custom architectures that have much better cost tradeoffs than general purpose computing architectures. This course deals with the analysis of such algorithms, and mapping them to architectures that are either custom designed or have specific extensions that make them better suited to certain kinds of operations.