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Rongjian Liang
NVIDIA
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Machine Learning for EDA
EDA for Machine Learning
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INSTA: An Ultra-Fast, Differentiable, Statistical Static Timing Analysis Engine for Industrial Physical Design Applications
DCO-3D: Differentiable Congestion Optimization in 3D ICs
Reinforcement Learning-Driven Window Selection for Enhanced Window-Based Rip-up and Reroute in Chip Detailed Routing
Hybrid Structured Clock Network Construction with GPUs for Large Designs
LEGO-Size: LLM-Enhanced GPU-Optimized Signoff-Accurate Differentiable VLSI Gate Sizing in Advanced Nodes
GOALPlace: Begin with the End in Mind
Hybrid Exact and Heuristic Efficient Transistor Network Optimization for Multi-Output Logic
Invited: ISPD 2025 Performance-Driven Large Scale Global Routing Contest
DiMO-CNN: Deep Learning Toolkit-Accelerated Analytical Modeling and Optimization of CNN Hardware and Dataflow
Invited Paper: 2024 ICCAD CAD Contest Problem C: Scalable Logic Gate Sizing using ML Techniques and GPU Acceleration
ReLS: Retrieval Is Efficient Knowledge Transfer For Logic Synthesis
Learning to Compare Hardware Designs for High-Level Synthesis
OpenROAD and CircuitOps: Infrastructure for ML EDA Research and Education
DiMO-Sparse: Differentiable Modeling and Optimization of Sparse CNN Dataflow and Hardware Architecture
GPU/ML-Enhanced Large Scale Global Routing Contest
MedPart: A Multi-Level Evolutionary Differentiable Hypergraph Partitioner
CircuitOps: An ML Infrastructure Enabling Generative AI for VLSI Circuit Optimization
Late Breaking Results: Test Selection For RTL Coverage By Unsupervised Learning From Fast Functional Simulation
BufFormer: A Generative ML Framework for Scalable Buffering
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