Timing and Congestion Driven Algorithms for FPGA Placement

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Placement is one of the most important steps in physical design for VLSI circuits. For field programmable gate arrays (FPGAs), the placement step determines the location of each logic block. I present novel timing and congestion driven placement algorithms for FPGAs with minimal runtime overhead. By predicting the post-routing timing-critical edges and estimating congestion accurately, this algorithm is able to simultaneously reduce the critical path delay and the minimum number of routing tracks. The core of the algorithm consists of a criticality-history record of connection edges and a congestion map. This approach is applied to the 20 largest Microelectronics Center ... continued below

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Zhuo, Yue December 2006.

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  • Zhuo, Yue

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Placement is one of the most important steps in physical design for VLSI circuits. For field programmable gate arrays (FPGAs), the placement step determines the location of each logic block. I present novel timing and congestion driven placement algorithms for FPGAs with minimal runtime overhead. By predicting the post-routing timing-critical edges and estimating congestion accurately, this algorithm is able to simultaneously reduce the critical path delay and the minimum number of routing tracks. The core of the algorithm consists of a criticality-history record of connection edges and a congestion map. This approach is applied to the 20 largest Microelectronics Center of North Carolina (MCNC) benchmark circuits. Experimental results show that compared with the state-of-the-art FPGA place and route package, the Versatile Place and Route (VPR) suite, this algorithm yields an average of 8.1% reduction (maximum 30.5%) in the critical path delay and 5% reduction in channel width. Meanwhile, the average runtime of the algorithm is only 2.3X as of VPR.

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  • December 2006

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  • May 5, 2008, 3:07 p.m.

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  • Jan. 15, 2014, 2:03 p.m.

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Zhuo, Yue. Timing and Congestion Driven Algorithms for FPGA Placement, thesis, December 2006; Denton, Texas. (digital.library.unt.edu/ark:/67531/metadc5423/: accessed November 25, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; .