Recent #machine learning news in the semiconductor industry

6 months ago
➀ The Technical University of Graz (TU Graz) is developing a real-time lightning risk assessment system to improve safety at outdoor events and construction sites. ➁ The system uses a network of field meters and combines data from lightning location systems and weather radar to predict lightning strikes. ➂ The research aims to reduce downtime and enhance safety by providing more accurate forecasts.
SafetyTU Grazmachine learning
6 months ago

➀ The current state-of-the-art machine learning applications use deep neural network models that have become so large and complex that they have exceeded the limits of traditional electronic computing hardware.

➁ Researchers at MIT and other institutions have developed a new type of photonic chip that can overcome these barriers.

➂ The optical device can complete the key calculations of machine learning classification tasks in less than half a nanosecond while achieving an accuracy rate of over 92%.

machine learning
6 months ago
➀ The Fraunhofer Institute for Production Systems and Design Technology IPK presents research projects and solutions addressing the challenges of modern manufacturing; ➁ The focus is on data management, flexible manufacturing processes, machine efficiency, human resource support, and sustainable production; ➂ The article highlights the importance of holistic system solutions and interdisciplinary collaboration in research and innovation.
AIIndustry 4.0ManufacturingResearch and Developmentinnovationmachine learningsustainability
7 months ago
➀ Alex Bronstein, known for groundbreaking innovations, starts his professorship at the Institute of Science and Technology Austria (ISTA) with a focus on Machine Learning in biowissenschaften. ➁ Bronstein has a background in both academic research at the Technion, Israel, and industry at Intel, where he developed 3D sensing technology. ➂ He aims to expand the boundaries of machine learning for applications in biowissenschaften and contribute to strategic directions in structural and cell biology, as well as single-cell analyses.
AITechnology DevelopmentTechnology Transfercomputer visioninnovationmachine learningresearch
8 months ago
➀ Researchers from the Leibniz Institute for Astrophysics Potsdam (AIP) and the Institute of Cosmos Sciences at the University of Barcelona (ICCUB) have used a novel machine learning model to process observation data from 217 million stars of the Gaia mission efficiently. The results are comparable to conventional methods for determining stellar parameters. The new approach opens up exciting possibilities for mapping properties like interstellar extinction and metallicity across the Milky Way, contributing to understanding the stellar populations and the structure of our galaxy. ➁ The third data release of the Gaia satellite by the European Space Agency ESA provided access to improved measurements for 1.8 billion stars, a vast amount of data for studying the Milky Way. Efficient analysis of such a large dataset, however, presents a challenge. The study published now investigates the use of machine learning to determine important stellar properties based on Gaia's spectrophotometric data. The model was trained on high-quality data from 8 million stars and achieved reliable predictions with low uncertainties. ➂ The machine learning technique, 'Extreme Gradient-Boosted Trees,' enables the determination of precise stellar properties like temperature, chemical composition, and interstellar dust extinction with unprecedented efficiency. The developed machine learning model, SHBoost, completes its tasks, including model training and prediction, within four hours on a single graphics processor - a process that previously required two weeks and 3000 high-performance processors.
Astronomymachine learning
8 months ago
➀ The article presents a novel technique for the on-demand nanoengineering of in-plane ferroelectric topologies; ➁ The technique involves a combination of scanning probe microscopy and machine learning algorithms; ➂ The research demonstrates the ability to create complex patterns and functionalities by manipulating the polarization in ferroelectric materials; ➃ The findings could lead to new applications in electronics, sensors, and energy storage.
machine learning
8 months ago
➀ Sentienz's Akiro IoT platform addresses challenges in e-transportation with advanced analytics and MQTT+; ➁ Akiro optimises IoT outcomes through messaging, analytics, and AI/ML; ➂ The platform supports smart meters, EV chargers, and battery monitoring systems, enhancing EV charging infrastructure and data management.
AIEV ChargingIoTmachine learningsecurity
9 months ago
➀ Meta Platforms identifies a widening gap between computing power and interconnect bandwidth at the 2022 OCP Global Summit. ➁ Broadcom proposes SCIP (Silicon Photonics in Package) as a solution to bridge this gap, focusing on low-cost, high-performance, and low-power interconnects. ➂ SCIP utilizes TSV technology and detachable optical connectors to achieve shorter interconnect distances and higher energy efficiency, targeting AI and machine learning applications.
AISilicon Photonicsmachine learning