3 citations · 3 across the 6 of their papers we have counts for
6 papers
Automated QoR improvement in OpenROAD with coding agents
Amur Ghose, Junyeong Jang, Andrew B. Kahng +1
EDA development and innovation has been constrained by scarcity of expert engineering resources. While leading LLMs have demonstrated excellent performance in coding and scientific…
ORFS-agent: Tool-Using Agents for Chip Design Optimization
Amur Ghose, Andrew B. Kahng, Sayak Kundu +1
Machine learning has been widely used to optimize complex engineering workflows across numerous domains. In integrated circuit design, modern flows (e.g., register-transfer level t…
GraSS: Combining Graph Neural Networks with Expert Knowledge for SAT Solver Selection
Zhanguang Zhang, Didier Chetelat, Joseph Cotnareanu +7
Boolean satisfiability (SAT) problems are routinely solved by SAT solvers in real-life applications, yet solving time can vary drastically between solvers for the same instance. Th…
Spectral Augmentations for Graph Contrastive Learning
Amur Ghose, Yingxue Zhang, Jianye Hao +1
Contrastive learning has emerged as a premier method for learning representations with or without supervision. Recent studies have shown its utility in graph representation learnin…
Generalizable Cross-Graph Embedding for GNN-based Congestion Prediction
Amur Ghose, Vincent Zhang, Yingxue Zhang +3
Presently with technology node scaling, an accurate prediction model at early design stages can significantly reduce the design cycle. Especially during logic synthesis, predicting…
Batch norm with entropic regularization turns deterministic autoencoders into generative models
Amur Ghose, Abdullah Rashwan, Pascal Poupart
The variational autoencoder is a well defined deep generative model that utilizes an encoder-decoder framework where an encoding neural network outputs a non-deterministic code for…