The Synchronous Data Flow (SDF) model is widely used for specifying signal processing or streaming applications. Since modern embedded applications become more complex with dynamic behavior changes at runtime, several extensions of the SDF model have been proposed to specify the dynamic behavior changes while preserving static analyzability of the SDF model. They assume that an application has a finite number of behaviors (or modes), and each behavior (mode) is represented by an SDF graph. They are classified as multi-mode dataflow models in this article. While there exist several scheduling techniques for multi-mode dataflow models, no one allows task migration between modes. By observing that the resource requirement can be additionally reduced if task migration is allowed, we propose a multiprocessor scheduling technique of a multi-mode dataflow graph considering task migration between modes. Based on a genetic algorithm, the proposed technique schedules all SDF graphs in all modes simultaneously to minimize the resource requirement. To satisfy the throughput constraint, the proposed technique calculates the actual throughput requirement of each mode and the output buffer size for tolerating throughput jitter. We compare the proposed technique with a method that analyzes SDF graphs in each execution mode separately, a method that does not allow task migration, and a method that does not allow mode-overlapped schedule for synthetic examples and five real applications: H.264 decoder, lane detection, vocoder, MP3 decoder, and printer pipeline.
Jung et al. REF proposed a multi-mode dataflow model and its mapping/scheduling technique on multiprocessor systems.
Multiprocessor Scheduling of a Multi-Mode Dataflow Graph Considering Mode Transition Delay
TODE
Computer Science