Researchers from 91°µÍø and the University of Central Florida have extended an evolutionary approach for training spiking neural networks.
The researchers from ORNL have developed a new and faster algorithm for the graph all-pair shortest-path (APSP) problem.
A team of researchers from 91°µÍø applied advanced statistical methods from biomedical research to study an unexpected failure mode of general-purpose computing on graphics processing units (GPGPUs).
Metal halide perovskites are promising materials for optoelectronic and sensing applications.
Researchers developed a novel algorithm for resilient and communication-efficient parallel matrix multiplication in HPC systems.
Researchers built a deep neural network to estimate the compressibility of scientific data.
To help expedite the use of quantum processing units, ORNL researchers developed an advanced software framework.
A team of ORNL researchers has used the DCA++ application, a popular code for predicting the performance of quantum materials, to verify two performance-enhancing strategies.
Kokkos is a programming model and library for writing performance-portable code in C++.
A new method was developed for the discovery of fundamental descriptors for gas adsorption through deep learning neural network (DNN) approach. This approach has great potential to identify structural parameters for gas adsorption.