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Wibheda: Framework for Data Dependency-Aware Multi-Constrained Hardware-Software Partitioning in FPGA-Based SoCs for IoT Devices.

, , , , and . FCCM, page 213. IEEE Computer Society, (2018)

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Power-Efficient Mapping of Large Applications on Modern Heterogeneous FPGAs., , , and . IEEE Trans. Comput. Aided Des. Integr. Circuits Syst., 40 (12): 2508-2521 (2021)Performance Estimation of FPGA Modules for Modular Design Methodology Using Artificial Neural Network., , and . ARC, volume 10824 of Lecture Notes in Computer Science, page 105-118. Springer, (2018)Communication-Aware Module Placement for Power Reduction in Large FPGA Designs., , , and . ISVLSI, page 209-214. IEEE Computer Society, (2018)Wibheda: Framework for Data Dependency-Aware Multi-Constrained Hardware-Software Partitioning in FPGA-Based SoCs for IoT Devices., , , , and . FCCM, page 213. IEEE Computer Society, (2018)Wibheda+: Framework for Data Dependency-aware Multi-constrained Hardware-Software Partitioning in FPGA-based SoCs for IoT Applications., , , , and . HEART, page 3:1-3:6. ACM, (2018)Communication-aware Partitioning for Energy Optimization of Large FPGA Designs., , , and . ACM Great Lakes Symposium on VLSI, page 407-410. ACM, (2017)Ant Colony Optimization based Module Footprint Selection and Placement for Lowering Power in Large FPGA Designs., , , and . ReConFig, page 1-8. IEEE, (2018)Mapping large-scale systems on to high density FPGAs. Nanyang Technological University, Singapore, (2020)