3 papers
cs.AI2026
Design Space Exploration of Reinforcement Learning Configurations for Post-Legalization Placement
Foteini Oikonomou, Maria Pantazi-Kypraiou, George Goudroumanis +5
Graph Neural Networks (GNNs) and Reinforcement Learning (RL) have recently been explored for VLSI placement optimization, but their effectiveness for post-legalization placement re…
cs.AI2026
GPlaceRL: An Open-Source Graph Reinforcement Learning Framework for Detailed Placement
Pavlos Stoikos, Foteini Oikonomou, Christos Poulos +5
Reinforcement learning (RL) has emerged as a promising approach for placement optimization, particularly when combined with graph neural networks (GNNs) that capture circuit connec…
cs.IR2019
Re-ranking Based Diversification: A Unifying View
Shameem A Puthiya Parambath
We analyze different re-ranking algorithms for diversification and show that majority of them are based on maximizing submodular/modular functions from the class of parameterized c…