Dynamic Reconfiguration to Support Concurrent Applications
IEEE Transactions on Computers
Reconfigurable computing: a survey of systems and software
ACM Computing Surveys (CSUR)
Optimization of Dynamic Hardware Reconfigurations
The Journal of Supercomputing
Fast Template Placement for Reconfigurable Computing Systems
IEEE Design & Test
Multitasking on FPGA Coprocessors
FPL '00 Proceedings of the The Roadmap to Reconfigurable Computing, 10th International Workshop on Field-Programmable Logic and Applications
Chip-Based Reconfigurable Task Management
FPL '01 Proceedings of the 11th International Conference on Field-Programmable Logic and Applications
Operating Systems for Reconfigurable Embedded Platforms: Online Scheduling of Real-Time Tasks
IEEE Transactions on Computers
The Erlangen Slot Machine: A Highly Flexible FPGA-Based Reconfigurable Platform
FCCM '05 Proceedings of the 13th Annual IEEE Symposium on Field-Programmable Custom Computing Machines
Real-Time Management of Hardware and Software Tasks for FPGA-based Embedded Systems
IEEE Transactions on Computers
Schedulability analysis of preemptive and nonpreemptive EDF on partial runtime-reconfigurable FPGAs
ACM Transactions on Design Automation of Electronic Systems (TODAES)
Online Task Scheduling for the FPGA-Based Partially Reconfigurable Systems
ARC '09 Proceedings of the 5th International Workshop on Reconfigurable Computing: Architectures, Tools and Applications
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Reconfigurable hardware devices, such as FPGAs, are increasingly used in embedded systems. To utilize these devices for real-time work loads, scheduling techniques are required that generate predictable task timings. In this paper, we present a partitioning-EDF (earliest deadline first) approach to find such schedules. The FPGA area is partitioned along one dimension into slots. The tasks are partitioned into groups. Then, each group is scheduled to exactly one slot using the EDF rule. We show that the problem of finding an optimal partitioning is related to the well-known 2-dimensional level bin-packing problem. We extend a previously reported ILP model to solve our partitioning problem to optimality. By a simulation study we demonstrate that the partitioning-EDF approach is able to find feasible schedules for most task sets with a system utilization of up to 70%. Additionally, we allow a task to be realized in alternative implementations. A simulation study reveals that the scheduling performance increases considerably if three instead of one task variants are considered. Finally, we model and study the impact of the device reconfiguration time on the scheduling performance.