Static scheduling of synchronous data flow programs for digital signal processing
IEEE Transactions on Computers
Compile-Time Scheduling and Assignment of Data-Flow Program Graphs with Data-Dependent Iteration
IEEE Transactions on Computers
Performance Estimation for Real-Time Distributed Embedded Systems
IEEE Transactions on Parallel and Distributed Systems
Scheduling Algorithms for Multiprogramming in a Hard-Real-Time Environment
Journal of the ACM (JACM)
Performance estimation for embedded systems with data and control dependencies
CODES '00 Proceedings of the eighth international workshop on Hardware/software codesign
Scheduling Dependent Tasks with Different Arrival Times to Meet Deadlines
Proceedings of the International Workshop organized by the Commision of the European Communities on Modelling and Performance Evaluation of Computer Systems
IEEE Transactions on Signal Processing
SPI: a system model for heterogeneously specified embedded systems
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Proceedings of the 40th annual Design Automation Conference
Gradual Relaxation Techniques with Applications to Behavioral Synthesis
Proceedings of the 2003 IEEE/ACM international conference on Computer-aided design
Concurrent topology and routing optimization in automotive network integration
Proceedings of the 45th annual Design Automation Conference
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Synchronous Data Flow (SDF) is a well-known model of computation that is widely used in the control engineering and digital signal processing domains. Existing scheduling methods are mainly static approaches that assume full knowledge of the environment, e.g. data arrival times. In a growing number of practical cases like internet multimedia applications there exists only partial knowledge of the environment, e.g. average data rates. Here, only dynamic scheduling can yield optimal results. In this paper, we propose a new dynamic scheduling method that minimizes the maximal response time of the system. It is a generalization of a deadline revision method to allow treatment of data-dependent tasks using EDF scheduling. The applicability and benefit of the new approach is shown using a real-world example.