Search Results for author: Saad Godil

Found 5 papers, 2 papers with code

PrefixRL: Optimization of Parallel Prefix Circuits using Deep Reinforcement Learning

no code implementations14 May 2022 Rajarshi Roy, Jonathan Raiman, Neel Kant, Ilyas Elkin, Robert Kirby, Michael Siu, Stuart Oberman, Saad Godil, Bryan Catanzaro

Deep Convolutional RL agents trained on this environment produce prefix adder circuits that Pareto-dominate existing baselines with up to 16. 0% and 30. 2% lower area for the same delay in the 32b and 64b settings respectively.

reinforcement-learning

Guiding Global Placement With Reinforcement Learning

no code implementations6 Sep 2021 Robert Kirby, Kolby Nottingham, Rajarshi Roy, Saad Godil, Bryan Catanzaro

In this work we augment state-of-the-art, force-based global placement solvers with a reinforcement learning agent trained to improve the final detail placed Half Perimeter Wire Length (HPWL).

reinforcement-learning

Can Q-Learning with Graph Networks Learn a Generalizable Branching Heuristic for a SAT Solver?

1 code implementation NeurIPS 2020 Vitaly Kurin, Saad Godil, Shimon Whiteson, Bryan Catanzaro

While more work is needed to apply Graph-Q-SAT to reduce wall clock time in modern SAT solving settings, it is a compelling proof-of-concept showing that RL equipped with Graph Neural Networks can learn a generalizable branching heuristic for SAT search.

Feature Engineering Q-Learning

Can $Q$-Learning with Graph Networks Learn a Generalizable Branching Heuristic for a SAT Solver?

2 code implementations26 Sep 2019 Vitaly Kurin, Saad Godil, Shimon Whiteson, Bryan Catanzaro

While more work is needed to apply Graph-$Q$-SAT to reduce wall clock time in modern SAT solving settings, it is a compelling proof-of-concept showing that RL equipped with Graph Neural Networks can learn a generalizable branching heuristic for SAT search.

Feature Engineering Q-Learning

Improving SAT Solver Heuristics with Graph Networks and Reinforcement Learning

no code implementations25 Sep 2019 Vitaly Kurin, Saad Godil, Shimon Whiteson, Bryan Catanzaro

We present GQSAT, a branching heuristic in a Boolean SAT solver trained with value-based reinforcement learning (RL) using Graph Neural Networks for function approximation.

Feature Engineering reinforcement-learning

Cannot find the paper you are looking for? You can Submit a new open access paper.