The pact is more broadly about removing the obstacles to running high-performance AI on IoT devices. The companies touted the potential for the tie-up, which gives the more than 30 million developers ...
Materials with advanced customized properties drive innovation in a number of real-life applications across various fields, such as information technology, transportation, green energy and health ...
Electron density prediction for a four-million-atom aluminum system using machine learning, deemed to be infeasible using traditional DFT method. × Researchers from Michigan Tech and the University of ...
Electrical power systems engineers need practical methods for predicting solar output power under varying environmental conditions of a single panel. By integrating an Arduino-based real-time data ...
Conducting polymers have emerged as a pivotal class of materials for advanced optoelectronic applications owing to their tunable molecular structure, ...
SiFive has announced a pair of high-end RISC-V cores for AI and machine learning in consumer, automotive and infrastructure markets. “Performance P870 and Intelligence X390 offer a new level of low ...
More aggressive feature scaling and increasingly complex transistor structures are driving a steady increase in process complexity, increasing the risk that a specified pattern may not be ...
Like other EDA vendors, Siemens EDA has over the past few years been adding artificial intelligence and machine learning capabilities to its suite of EDA (electronic design automation) tools. At the ...
In this special guest feature, Ori Geva, Co-Founder and CEO of Medial EarlySign, discusses how the ue of machine learning can help create new opportunities for earlier intervention and delivery of ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results
Feedback