tag: GNNs

Using Diffusion Models to Generate Chip Placements (UC Berkeley)
By Technical Paper Link - 27 Jul, 2024 - Comments: 0

A technical paper titled “Chip Placement with Diffusion” was published by researchers at UC Berkeley. Abstract: "Macro placement is a vital step in digital circuit design that defines the physical location of large collections of components, known as macros, on a 2-dimensional chip. The physical layout obtained during placement determines key performance metrics of the chip, such as power... » read more

Deep Learning Discovers Millions Of New Materials (Google)
By Technical Paper Link - 06 Dec, 2023 - Comments: 0

A technical paper titled “Scaling deep learning for materials discovery” was published by researchers at Google DeepMind and Google Research. Abstract: "Novel functional materials enable fundamental breakthroughs across technological applications from clean energy to information processing. From microchips to batteries and photovoltaics, discovery of inorganic crystals has been bottleneck... » read more

Predicting Defect Properties In Semiconductors With Graph Neural Networks
By Technical Paper Link - 24 Oct, 2023 - Comments: 0

A technical paper titled “Accelerating Defect Predictions in Semiconductors Using Graph Neural Networks” was published by researchers at Purdue University, Indian Institute of Technology (IIT) Madras, GE Research, and National Institute of Standards and Technology (NIST). Abstract: "Here, we develop a framework for the prediction and screening of native defects and functional impurities i... » read more

Deep Learning Applications For Material Sciences: Methods, Recent Developments
By Technical Paper Link - 10 Jun, 2022 - Comments: 0

New technical paper titled "Recent advances and applications of deep learning methods in materials science" from researchers at NIST, UCSD, Lawrence Berkeley National Laboratory, Carnegie Mellon University, Northwestern University, and Columbia University. Abstract "Deep learning (DL) is one of the fastest-growing topics in materials data science, with rapidly emerging applications spanning... » read more

There’s More To Machine Learning Than CNNs
By Bryon Moyer - 09 Jun, 2021 - Comments: 1

Neural networks – and convolutional neural networks (CNNs) in particular – have received an abundance of attention over the last few years, but they're not the only useful machine-learning structures. There are numerous other ways for machines to learn how to solve problems, and there is room for alternative machine-learning structures. “Neural networks can do all this really comple... » read more

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