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Clock-aware ultrascale FPGA placement with machine learning routability prediction: (Invited paper).

, , , , and . ICCAD, page 929-936. IEEE, (2017)

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Lagrangian Relaxation-Based Time-Division Multiplexing Optimization for Multi-FPGA Systems., and . ICCAD, page 1-8. ACM, (2019)CUGR: Detailed-Routability-Driven 3D Global Routing with Probabilistic Resource Model., , , and . DAC, page 1-6. IEEE, (2020)Simultaneous Reconnection Surgery Technique of Routing With Machine Learning-Based Acceleration., , and . IEEE Trans. Comput. Aided Des. Integr. Circuits Syst., 39 (6): 1245-1257 (2020)Heterogeneous Graph Neural Network-Based Imitation Learning for Gate Sizing Acceleration., , , , , , , , and . ICCAD, page 57:1-57:9. ACM, (2022)Legalization algorithm for multiple-row height standard cell design., , and . DAC, page 83:1-83:6. ACM, (2016)Multi-FPGA Co-optimization: Hybrid Routing and Competitive-based Time Division Multiplexing Assignment., , , and . ASP-DAC, page 176-182. ACM, (2021)Device Layer-Aware Analytical Placement for Analog Circuits., , , , , , , and . ISPD, page 19-26. ACM, (2019)RippleFPGA: a routability-driven placement for large-scale heterogeneous FPGAs., , , , , , , , and . ICCAD, page 67. ACM, (2016)Dr. CU: Detailed Routing by Sparse Grid Graph and Minimum-Area-Captured Path Search., , , and . IEEE Trans. Comput. Aided Des. Integr. Circuits Syst., 39 (9): 1902-1915 (2020)Lagrangian Relaxation-Based Time-Division Multiplexing Optimization for Multi-FPGA Systems., and . ACM Trans. Design Autom. Electr. Syst., 25 (2): 21:1-21:23 (2020)