Preparing for exascale: LLNL breaks ground on computing facility upgrades

To meet the needs of tomorrow’s supercomputers, the National Nuclear Security Administration’s (NNSA’s) Lawrence Livermore National Laboratory (LLNL) has broken ground on its Exascale Computing Facility Modernization (ECFM) project, which will substantially upgrade the mechanical and electrical capabilities of the Livermore Computing Center.

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Organic Memory Devices Show Promise for Flexible, Wearable, Personalized Computing

The advent of artificial intelligence, machine learning and the internet of things is expected to change modern electronics. The pressing question for many researchers is how to handle this technological revolution. Brain-inspired electronics with organic memristors could offer a functionally promising and cost- effective platform. Since memristors are functionally analogous to the operation of neurons, the computing units in the brain, they are optimal candidates for brain-inspired computing platforms.

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Recipe for Neuromorphic Processing Systems?

The field of “brain-mimicking” neuromorphic electronics shows great potential for basic research and commercial applications, and researchers in Germany and Switzerland recently explored the possibility of reproducing the physics of real neural circuits by using the physics of silicon. In Applied Physics Letters, they present their work to understand neural processing systems, as well as a recipe to reproduce these computing principles in mixed signal analog/digital electronics and novel materials.

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LLNL and HPE to partner with AMD on El Capitan, projected as world’s fastest supercomputer

Lawrence Livermore National Laboratory (LLNL), Hewlett Packard Enterprise (HPE) and Advanced Micro Devices, Inc. (AMD) today announced the selection of AMD as the node supplier for El Capitan, projected to be the world’s most powerful supercomputer when it is fully deployed in 2023.

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ORNL researchers develop ‘multitasking’ AI tool to extract cancer data in record time

To better leverage cancer data for research, scientists at ORNL are developing an artificial intelligence (AI)-based natural language processing tool to improve information extraction from textual pathology reports. In a first for cancer pathology reports, the team developed a multitask convolutional neural network (CNN)—a deep learning model that learns to perform tasks, such as identifying key words in a body of text, by processing language as a two-dimensional numerical dataset.

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