Michigan Technological University

Researchers Apply Machine Learning and AI in Search of Less Costly Electronic Structure Method

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 ...
Electronic Design

Neuromorphic NPU Sips Power to Handle Edge Machine-Learning Models

What are spiking neural networks (SNNs)? Why the Akida Pico neural processing unit (NPU) can use so little power to handle machine-learning models. Why neuromorphic computing is important to ...
Nature

Machine Learning Applications in High-Speed Electronic Systems

Recent advancements in machine learning have significantly impacted the domain of high-speed electronic systems. By leveraging state‐of‐the‐art algorithms and novel network architectures, researchers ...
Nanowerk

When machine learning shows its reasoning, electrocatalyst discovery accelerates

Machine learning algorithms that output human-readable equations and design rules are transforming how electrocatalysts for ...
Electronic Design

Dynamically Reconfigurable Processors Accelerate Machine-Learning Apps

What is a dynamically reconfigurable processor (DRP)? How DRPs accelerate machine-learning applications. Why is the RZ/V2H well-suited for robotic apps? Renesas's RZ/V2H system-on-chip (SoC) is the ...
ascopubs.org

Electronic health records (EHR)-based machine learning (ML) approach to predict risk of progression to metastatic melanoma after initial diagnosis.

ctDNA versus 18F-FDG PET-CT in predicting long-term disease control in patients with advanced melanoma undergoing immune checkpoint blockade therapy. Delineating the role of the microbiome and tumor ...
AZoSensors on MSN

Machine-learning technique shown to reduce sensor drift in electronic noses

Research reveals that knowledge distillation significantly compensates for sensor drift in electronic noses, improving ...