PADE

Samuel Ward, Duo Ding, David Z. Pan
2012 Proceedings of the 49th Annual Design Automation Conference on - DAC '12  
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%
more » ... iner Wire Length (StWL) improvements on industrial hybrid benchmarks and at least 2% average total HPWL and 3% StWL improvements on ISPD 2005 contest benchmarks. To the best of our knowledge, this is the first attempt to link data learning, datapath extraction with evaluation, and placement and has the tremendous potential for pushing placement state-of-the-art for modern circuits which have datapath and random logics.
doi:10.1145/2228360.2228497 dblp:conf/dac/WardDP12 fatcat:xilgxaryzvetvc6wd3jptaq2ta