2/3/2021 Illinois ECE
CSL Professor Nam Sung Kim has been named a Fellow of the Association for Computing Machinery (ACM), the world s largest educational and scientific computing society. He is honored for contribution to design and modeling of power-efficient computer architectures.
According to the ACM, “the Fellows program recognizes the top 1% of ACM Members for their
outstanding accomplishments in computing and information technology and/or outstanding service to ACM and the larger computing community. Fellows are nominated by their peers, with nominations reviewed by a distinguished selection committee.” This year s Fellows class recognizes members from universities, corporations, and research centers in Australia, Austria, Canada, China, Germany, Israel, Japan, The Netherlands, South Korea, Spain, Sweden, Switzerland, Taiwan, the United Kingdom, and the United States.
Nanotechnology Now
Our NanoNews Digest Sponsors
Home > Press > Conductive nature in crystal structures revealed at magnification of 10 million times: University of Minnesota study opens up possibilities for new transparent materials that conduct electricity
University of Minnesota Professor K. Andre Mkhoyan and his team used analytical scanning transmission electron microscopy (STEM), which combines imaging with spectroscopy, to observe metallic properties in the perovskite crystal barium stannate (BaSnO3). The atomic-resolution STEM image, with a BaSnO3 crystal structure (on the left), shows an irregular arrangement of atoms identified as the metallic line defect core.
CREDIT
Mkhoyan Group, University of Minnesota
Abstract:
In groundbreaking materials research, a team led by University of Minnesota Professor K. Andre Mkhoyan has made a discovery that blends the best of two sought-after qualities for touchscreens and smart windows transparency and conductivity.
January 15, 2021 Using advanced analytical scanning transmission electron microscopy (STEM) at a magnification of 10 million times, University of Minnesota researchers were able to isolate and image the structure and composition of the metallic line defect in a perovskite crystal BaSnO3. This image shows the atomic arrangement of both the BaSnO3 crystal (on the left) and the metallic line defect.
In groundbreaking materials research, a team led by University of Minnesota Professor K. Andre Mkhoyan has made a discovery that blends the best of two sought-after qualities for touchscreens and smart windows transparency and conductivity.
The researchers are the first to observe metallic lines in a perovskite crystal. Perovskites abound in the Earth’s center, and barium stannate (BaSnO3) is one such crystal. However, it has not been studied extensively for metallic properties because of the prevalence of more conductive materials on the planet like metals or semiconductors
By Tom Abate
Smartwatches and other battery-powered electronics would be even smarter if they could run AI algorithms. But efforts to build AI-capable chips for mobile devices have so far hit a wall – the so-called “memory wall” that separates data processing and memory chips that must work together to meet the massive and continually growing computational demands imposed by AI.
Hardware and software innovations give eight chips the illusion that they’re one mega-chip working together to run AI. (Image credit: Stocksy / Drea Sullivan)
“Transactions between processors and memory can consume 95 percent of the energy needed to do machine learning and AI, and that severely limits battery life,” said computer scientist Subhasish Mitra, senior author of a new study published in
E-Mail
Smartwatches and other battery-powered electronics would be even smarter if they could run AI algorithms. But efforts to build AI-capable chips for mobile devices have so far hit a wall - the so-called memory wall that separates data processing and memory chips that must work together to meet the massive and continually growing computational demands imposed by AI. Transactions between processors and memory can consume 95 percent of the energy needed to do machine learning and AI, and that severely limits battery life, said computer scientist Subhasish Mitra, senior author of a new study published in
Nature Electronics.
Now, a team that includes Stanford computer scientist Mary Wootters and electrical engineer H.-S. Philip Wong has designed a system that can run AI tasks faster, and with less energy, by harnessing eight hybrid chips, each with its own data processor built right next to its own memory storage.