基于lut的FPGA映射的同时深度和面积最小化

J. Cong, Yean-Yow Hwang
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引用次数: 102

摘要

本文提出了对FlwoMap算法的改进,即CutMap,该算法通过对关键节点计算最小代价最小高度k可行切割来实现深度最小化,对非关键节点计算最小代价k可行切割来实现面积最小化,从而将映射过程中的深度最小化和面积最小化结合起来。与FlowMap算法一样,CutMap在多项式时间内保证深度最优映射解决方案,但使用的k - lut要少得多。我们已经实现了CutMap并在MCNC逻辑合成基准上进行了测试。对于深度最优映射解决方案,CutMap使用的k - lut比FlowMap少15%。我们还在FlowMap_r算法中测试了CutMap之后的深度松弛例程,通过深度松弛实现了面积最小化。CutMap紧随FlowMap_r之后,性能优于FlowMap_r。
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Simultaneous Depth and Area Minimization in LUT-based FPGA Mapping
In this paper, we present an improvement of the FlwoMap algorithm, named CutMap, which combines depth and area minimization during the mapping process by computing min-cost min-height K-feasible cuts for critical nodes for depth minimization and computing min-cost K-feasible cuts for non-critical nodes for area minimization. CutMap guarantees depth-optimal mapping solutions in polynomial time as the FlowMap algorithm but uses considerably fewer K-LUTs. We have implemented CutMap and tested it on the MCNC logic synthesis benchmarks. For depth-optimal mapping solutions, CutMap uses 15% fewer K-LUTs than FlowMap. We also tested CutMap followed by the depth relaxation routines in FlowMap_r algorithm, which achieves area minimization by depth relaxation. CutMap followed FlowMap_r performs better than FlowMap_r.
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