PADE: A high-performance placer with automatic datapath extraction and evaluation through high-dimensional data learning

@article{Ward2012PADEAH,
  title={PADE: A high-performance placer with automatic datapath extraction and evaluation through high-dimensional data learning},
  author={Samuel I. Ward and Duo Ding and David Z. Pan},
  journal={DAC Design Automation Conference 2012},
  year={2012},
  pages={756-761}
}
This work presents PADE, a new placer with automatic datapath extraction and evaluation. PADE applies novel data learning techniques to train, predict, and evaluate potential datapaths using high-dimensional data such as netlist symmetrical structures, initial placement hints and relative area. Extracted datapaths are mapped to bit-stack structures that are aligned and simultaneously placed with the random logic. Results show at least 7% average total Half-Perimeter Wire Length (HPWL) and 12… CONTINUE READING
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