Dynamic load balancing for distributed memory multiprocessors
Journal of Parallel and Distributed Computing
Partitioning sequential circuits on dynamically reconfiguable FPGAs
FPGA '98 Proceedings of the 1998 ACM/SIGDA sixth international symposium on Field programmable gate arrays
CORDS: hardware-software co-synthesis of reconfigurable real-time distributed embedded systems
Proceedings of the 1998 IEEE/ACM international conference on Computer-aided design
Optimal and Alternating-Direction Load Balancing Schemes
Euro-Par '99 Proceedings of the 5th International Euro-Par Conference on Parallel Processing
A Self-Tuning Cache Architecture for Embedded Systems
Proceedings of the conference on Design, automation and test in Europe - Volume 1
Distributed HW/SW-Partitioning for Embedded Reconfigurable Networks
Proceedings of the conference on Design, Automation and Test in Europe - Volume 2
Dynamic task binding for hardware/software reconfigurable networks
SBCCI '06 Proceedings of the 19th annual symposium on Integrated circuits and systems design
Modeling and design of fault-tolerant and self-adaptive reconfigurable networked embedded systems
EURASIP Journal on Embedded Systems
An operating system infrastructure for fault-tolerant reconfigurable networks
ARCS'06 Proceedings of the 19th international conference on Architecture of Computing Systems
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Today's embedded systems are typically distributed and more often confronted with time-varying demands. Existing methodologies that optimize the partitioning of computational tasks to hardware (HW) and software (SW) at compile-time become obsolete or inefficient in this context as the optimal use of existing resources cannot be foreseen. Here, we investigate a discrete iterative algorithm that balances the load of a HW/SW partition online: Once there are changing computational demands, the system will dynamically assign tasks to reconfigurable HW or SW resources and migrates tasks to other nodes if necessary. For this purpose an Evolutionary Algorithm combined with a discrete version of a diffusion algorithm is presented. Concerning the diffusion algorithm, we will show theoretically and by experiment that our version is run-time optimal in a linear number of steps.