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 ...

The idea is that graph networks are bigger than any one machine-learning approach. Graphs bring an ability to generalize about structure that the individual neural nets don't have.

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 ...

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 ...

A new simulator simply called Evolution, which is available in a desktop browser and on the Play Store, uses a neural network to bring to life any creatures you can think to cobble together from ...

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