Multiprocessor Scheduling of a Multi-mode Dataflow Graph Considering Mode Transition Delay
Hanwoong Jung, Hyunok Oh, and Soonhoi Ha

TL;DR
This paper introduces a multiprocessor scheduling method for multi-mode dataflow graphs that incorporates task migration, aiming to reduce resource usage while satisfying throughput constraints in dynamic embedded applications.
Contribution
It proposes a novel genetic algorithm-based scheduling technique that allows task migration across modes, optimizing resource utilization for multi-mode dataflow models.
Findings
Reduces resource requirements compared to existing methods
Effectively manages throughput constraints and jitter
Demonstrates improvements on real application benchmarks
Abstract
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 run-time, 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 paper. 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…
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