Neural Network Models in Semiconductor Device Simulation Publication Trend The graph below shows the total number of publications each year in Neural Network Models in Semiconductor Device Simulation.

In a white paper, researchers at Bloomberg modeled supply chain data as a graph and used GNNs to create a long-short portfolio. The results demonstrate an edge over traditional approaches.

BingoCGN, a scalable and efficient graph neural network accelerator that enables inference of real-time, large-scale graphs through graph partitioning, has been developed by researchers at ...

A team of chemistry, life science, and AI researchers are using graph neural networks to identify molecules and predict smells. Models made by researchers outperform current state-of-the-art ...

Neural modeling and simulation are foundational tools in computational neuroscience, enabling researchers to explore how neural systems process information, ...

Facebook releases AI Habitat, a powerful simulator for training neural networks - SiliconANGLEAI Habitat might not be the first simulator built with machine learning projects in mind, but it’s ...

Expect to hear increasing buzz around graph neural network use cases among hyperscalers in the coming year. Behind the scenes, these are already replacing existing recommendation systems and traveling ...

To address these limitations, we introduce a novel framework: the Molecular Merged Hypergraph Neural Network (MMHNN). MMHNN ...

Artificial Intelligence Industrial Design Software The Future of engineering goes from numerical simulation to neural networks Opinion The shift from slow, manual simulation to fast, automated ...