Low-power heterogeneous computing via adaptive execution of dataflow actors

Dataflow models of computation have been shown to provide an excellent basis for describing signal processing applications and mapping them to heterogeneous computing platforms that consist of multicore CPUs and graphics processing units (GPUs). Recently several efficient dataflow-based programming frameworks have been introduced for such needs. Most of contemporary signal processing applications can be described using static dataflow models of computation (e.g. synchronous dataflow) that have desirable features such as compile-time analyzability. Unfortunately, static dataflow models of computation turn out to be restrictive when applications need to adapt their behavior to varying conditions at run-time, such as power saving through adaptive processing. This paper analyzes three dataflow approaches for implementing adaptive application behavior in terms of expressiveness and efficiency. The focus of the paper is on heterogeneous computing platforms and particularly on adapting application processing for achieving power saving. Experiments are conducted with deep neural network and dynamic predistortion applications on two platforms: a mobile multicore SoC and a GPU-equipped workstation.