MLBuf-RePlAce uses a recursive generative model to predict buffer placements during analytical global placement, reporting up to 56% better post-route total negative slack than OpenROAD's default in its open-source-flow benchmarks.
Toward an Open-Source Digital Flow: First Learnings from the OpenROAD Project
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Recursive Learning-Based Virtual Buffering for Analytical Global Placement
MLBuf-RePlAce uses a recursive generative model to predict buffer placements during analytical global placement, reporting up to 56% better post-route total negative slack than OpenROAD's default in its open-source-flow benchmarks.