Generating Compact Code from Dataflow Specifications of Multirate Signal Processing Algorithms

Abstract

Synchronous dataflow (SDF) semantics are wellsuited to representing and compiling multirate signal processing algorithms. A key to this match is the ability to cleanly express iteration without overspecifying the execution order of computations, thereby allowing efficient schedules to be constructed. Due to limited program memory, it is often desirable to translate the iteration in an SDF graph into groups of repetitive Wring patterns so that loops can be constructed in the target code. This paper establishes fundamental topological relationships between iteration and looping in SDF graphs, and presents a scheduling framework that provably synthesizes the most compact looping structures for a large class of practical SDF graphs. By modularizing different components of the scheduling framework, and establishing their independence, we show how other scheduling objectives, such as minimizing data buffering requirements or increasing the number of data transfers that occur in registers, can be incorporated in a manner that does not conflict with the goal of code compactness.