Buffer minimization and time-multiplexed I/O on dynamically reconfigurable FPGAs
FPGA '97 Proceedings of the 1997 ACM fifth international symposium on Field-programmable gate arrays
Scheduling designs into a time-multiplexed FPGA
FPGA '98 Proceedings of the 1998 ACM/SIGDA sixth international symposium on Field programmable gate arrays
Partitioning sequential circuits on dynamically reconfiguable FPGAs
FPGA '98 Proceedings of the 1998 ACM/SIGDA sixth international symposium on Field programmable gate arrays
Network flow based circuit partitioning for time-multiplexed FPGAs
Proceedings of the 1998 IEEE/ACM international conference on Computer-aided design
Circuit partitioning for dynamically reconfigurable FPGAs
FPGA '99 Proceedings of the 1999 ACM/SIGDA seventh international symposium on Field programmable gate arrays
Performance-Oriented Fully Routable Dynamic Architecture for a Field
Performance-Oriented Fully Routable Dynamic Architecture for a Field
A clustering- and probability-based approach for time-multiplexed FPGA partitioning
Integration, the VLSI Journal
Temporal partitioning data flow graphs for dynamically reconfigurable computing
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
A minimum communication cost algorithm for dynamically reconfigurable computing system
CSS '07 Proceedings of the Fifth IASTED International Conference on Circuits, Signals and Systems
Combining temporal partitioning and temporal placement techniques for communication cost improvement
Advances in Engineering Software
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This paper presents an optimal algorithm to solve the schedule compression problem, which is an open problem proposed by Trimberger [1] for time-multiplexed FPGA partitioning. Time-multiplexed FPGAs have the potential to dramatically improve logic density by time-sharing logic. Schedule compression is an important step in partitioning for time-multiplexed FPGAs [1,4,9,10] and can greatly influence the quality of the partitioning solution. We exactly solve the schedule compression problem by converting it to a constrained min-max path problem. We further extend our algorithm to minimize the communication cost during schedule compression. Experiments show that our optimal algorithm outperforms the existing heuristics and runs very efficiently.