Topic
- Computing and Processing
- Components, Circuits, Devices and Systems
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- Power, Energy and Industry Applications
- Signal Processing and Analysis
- Robotics and Control Systems
- General Topics for Engineers
- Fields, Waves and Electromagnetics
- Engineered Materials, Dielectrics and Plasmas
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- English for Technical Professionals
Saad Godil
Affiliation
NVIDIA, Santa Clara, CA, USA
Topic
Design Space,Electronic Design Automation,Physical Synthesis,Quality Of Outcomes,Chip Design,Circuit Area,Convolutional Neural Network,Deep Reinforcement Learning,Design Space Exploration,Graph Attention Network,Graph Neural Networks,Half Adder,Multilayer Perceptron,Physical Design,Reinforcement Learning Agent,Simulated Annealing,State Space,Synthesis Tool,Accurate Estimation,Advanced Machine Learning,Algorithmic Framework,Analysis Of Metrics,Application Of Reinforcement Learning,Area Metrics,Average Mean Absolute Error,Bayesian Optimization,Cell Library,Circuit Power,Classification Task,Commercial Tools,Complex Decision,Convolutional Network,Coverage Goals,Coverage Metrics,Deep Convolutional Neural Network,Deep Learning,Deep Neural Network,Deep Q-learning,Deep Q-network,Delay Changes,Design Flow,Detailed Routing,Directed Acyclic Graph,Estimation Of Metrics,Exhaustive Search,Final Metric,First-in-first-out,Fitness Function,Fully Convolutional Network,Future Design,
Biography
Saad Godil is the Director of Applied Deep Learning Research at NVIDIA. His research interests include reinforcement learning, graph neural networks, and ML applications for VLSI and Chip Design. Contact him at [email protected].