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
Managing pipeline-reconfigurable FPGAs
FPGA '98 Proceedings of the 1998 ACM/SIGDA sixth 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
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
Partitioning Sequential Circuits on Dynamically Reconfigurable FPGAs
IEEE Transactions on Computers
Temporal Partitioning and Scheduling Data Flow Graphs for Reconfigurable Computers
IEEE Transactions on Computers
Loop Pipelining and Optimization for Run Time Reconfiguration
IPDPS '00 Proceedings of the 15 IPDPS 2000 Workshops on Parallel and Distributed Processing
An optimal algorithm for minimizing run-time reconfiguration delay
ACM Transactions on Embedded Computing Systems (TECS)
An integrated data flow visual language and software development environment
Journal of Visual Languages and Computing
Multi-FPGA partitioning method based on topological levelization
Journal of Electrical and Computer Engineering
Performance of partial reconfiguration in FPGA systems: A survey and a cost model
ACM Transactions on Reconfigurable Technology and Systems (TRETS)
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This paper considers the problem of scheduling a chain of n coarse-grained tasks on a linear array of k reconfigurable FPGAs with the objective of primarily minimizing reconfiguration time. A high-level meta-algorithm along with two detailed metaalgorithms (GPRM and SPRM) that support a wide range of problem formulations and cost functions is presented. GPRM, the more general of the two schemes, reduces the problem to computing a shortest path in a DAG; SPRM, the less general scheme, employs dynamic programming. Both meta algorithms are linear in n and compute optimal solutions. GPRM can be exponential in k but is nevertheless practical because k is typically a small constant. The deterministic quality of this meta algorithm and the guarantee of optimal solutions for all of the formulations discussed make this approach a powerful alternative to other metatechniques such as simulated annealing and genetic algorithms.