Filter News
Area of Research
- (-) Nuclear Science and Technology (9)
- (-) Supercomputing (32)
- Advanced Manufacturing (2)
- Clean Energy (17)
- Climate and Environmental Systems (1)
- Computational Engineering (1)
- Computer Science (8)
- Fusion Energy (6)
- Materials (21)
- National Security (4)
- Neutron Science (4)
- Quantum information Science (3)
News Topics
- (-) Advanced Reactors (5)
- (-) Computer Science (29)
- (-) Critical Materials (1)
- (-) Cybersecurity (2)
- (-) Fusion (1)
- (-) Grid (1)
- (-) Molten Salt (3)
- (-) Quantum Science (5)
- (-) Space Exploration (4)
- 3-D Printing/Advanced Manufacturing (1)
- Artificial Intelligence (7)
- Big Data (4)
- Bioenergy (2)
- Biomedical (3)
- Energy Storage (1)
- Environment (2)
- Exascale Computing (2)
- Frontier (2)
- Isotopes (2)
- Materials Science (2)
- Nanotechnology (1)
- Neutron Science (2)
- Nuclear Energy (15)
- Physics (2)
- Polymers (1)
- Security (1)
- Summit (11)
- Sustainable Energy (2)
- Transportation (2)
Media Contacts
![ORNL’s Steven Young (left) and Travis Johnston used Titan to prove the design and training of deep learning networks could be greatly accelerated with a capable computing system. ORNL’s Steven Young (left) and Travis Johnston used Titan to prove the design and training of deep learning networks could be greatly accelerated with a capable computing system.](/sites/default/files/styles/list_page_thumbnail/public/news/images/RAvENNA%20release%20pic.png?itok=2bDpK5Mo)
A team of researchers from the Department of Energy’s Oak Ridge National Laboratory has married artificial intelligence and high-performance computing to achieve a peak speed of 20 petaflops in the generation and training of deep learning networks on the