Imagine a computer that can think as fast as the human brain while using very little energy. That’s the goal of scientists seeking to discover or develop “neuromorphic” materials that can send and process signals as easily as the brain’s neurons and synapses. In a paper just published scientists describe surprising new details about vanadium dioxide, one of the most promising neuromorphic materials.
The U.S. Department of Energy (DOE) recently awarded nearly $54 million to 10 new microelectronics research projects. Scientists Supratik Guha and Valerie Taylor at DOE’s Argonne National Laboratory will lead two of these projects.
Scientists at Sandia National Laboratories are creating a concept for a new kind of computer for solving complex probability problems that involve random chance.
Research from the lab of Shantanu Chakrabartty reveals constraints can lead to learning in AI systems.
A group of scientists from around the country, including those at Argonne National Laboratory, have discovered a way to make AI-related hardware more efficient and sustainable.
A team of researchers co-led by Berkeley Lab and Columbia University has developed a new material called avalanching nanoparticles that, when used as a microscopic probe, offers a simpler approach to taking high-resolution, real-time snapshots of a cell’s inner workings at the nanoscale.
Researchers developed a novel memory storage device that uses soft biomaterials to mimic synapses. The device consists of two layers of fatty organic compounds called lipids. The lipid layers form at an oil-water interface to create a soft membrane. When scientists apply an electric charge to the membrane, the membrane changes shape in ways that can store energy and filter biological and chemical data.
The novel system developed by National University of Singapore computer scientists and materials engineers combines an artificial brain system with human-like electronic skin, and vision sensors, to make robots smarter.
In a recent theoretical study, scientists discovered the presence of the Hopfion topological structure in nano-sized particles of ferroelectrics — materials with promising applications in microelectronics and information technology.
Research findings published in Nature Communications outline how a national team of researchers supported by the DOE’s Office of Science opens up a new dimension of safe hardware for AI and neuromorphic computing.
Researchers at Oak Ridge National Laboratory and the University of Tennessee achieved a rare look at the inner workings of polymer self-assembly at an oil-water interface to advance materials for neuromorphic computing and bio-inspired technologies.
Since 1947, computing development has seen a consistent doubling of the number of transistors that can fit on a chip. But that trend, Moore’s Law, may reach its limit as components of submolecular size encounter problems with thermal noise, making further scaling impossible. In this week’s Applied Physics Reviews, researchers present an examination of the computing landscape, focusing on functions needed to advance brain-inspired neuromorphic computing.
Several Argonne researchers will attend the Supercomputing 2019 (SC19) conference to share scientific computing advances and insights with an eye toward the upcoming exascale era.
Researchers at the Department of Energy’s Oak Ridge National Laboratory, the University of Tennessee and Texas A&M University demonstrated bio-inspired devices that accelerate routes to neuromorphic, or brain-like, computing.