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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 highdimensional 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%
doi:10.1145/2228360.2228497
dblp:conf/dac/WardDP12
fatcat:xilgxaryzvetvc6wd3jptaq2ta