Force Fields Will Accelerate Atomistic Simulations By 10,000× In 2026, Unlocking New Era Of Discovery

By Anders Blom - 19 Feb, 2026 - Comments: 0

By Anders Blom and Igor Markov “Force fields” have long captured our imagination — the invisible shields of science-fiction lore that protect starships and superheroes from harm. But in the world of scientific discovery, force fields play a much different role: They are mathematical models that let us peer into the atomic heart of matter itself. Now, thanks to breakthroughs in artif... » read more

HW-Triggered Backdoors Across Common GPU Accelerators (BIFOLD, TU Berlin, CISPA)

By Technical Paper Link - 02 Feb, 2026 - Comments: 0

A new technical paper titled "Hardware-Triggered Backdoors" was published by researchers at Berlin Institute for the Foundations of Learning and Data (BIFOLD), TU Berlin and CISPA Helmholtz Center for Information Security. Abstract "Machine learning models are routinely deployed on a wide range of computing hardware. Although such hardware is typically expected to produce identical result... » read more

Accelerating Semiconductor Innovation Through Machine Learning-Driven Modeling

By Keysight - 23 Dec, 2025 - Comments: 0

The semiconductor industry is entering an era of unprecedented complexity, driven by advanced architectures such as Gate-All-Around (GAA) transistors, wide-bandgap materials like GaN and SiC, and heterogeneous integration strategies. Traditional physics-based modeling approaches are increasingly challenged by nonlinear effects, electro-thermal interactions, and variability across device geometr... » read more

Benefits And Limits Of Using ML For Materials Discovery

By Katherine Derbyshire - 18 Dec, 2025 - Comments: 0

Machine learning tools can accelerate all stages of materials discovery, from initial screening to process development. Whether the goal is to identify new applications for known materials or to design new molecules for a particular task, these tools help materials scientists find correlations in large data libraries. Still, machine learning tools are not magic. “Software tools are only as... » read more

AI Techniques To Solve HW-SW Challenges For Useful Quantum Computing (Nvidia, U. of Oxford et al.)

By Technical Paper Link - 07 Dec, 2025 - Comments: 0

A new technical paper "Artificial intelligence for quantum computing" was published by researchers at NVIDIA, University of Oxford, University of Toronto, Quantum Motion, University of Waterloo et al. Abstract "Artificial intelligence (AI) advancements over the past few years have had an unprecedented and revolutionary impact across everyday application areas. Its significance also extend... » read more

AI With Open And Scaled Data Sharing in IC Manufacturing (NIST)

By Technical Paper Link - 02 Dec, 2025 - Comments: 2

A new workshop report titled "Artificial Intelligence with Open and Scaled Data Sharing in Semiconductor Manufacturing" was published by NIST. Abstract "The Workshop sponsored by the National Science Foundation (NSF) (NSF award 2334590, "Artificial Intelligence with Open and Scaled Data Sharing in the Semiconductor Industry") and supported by the National Institute of Standards and Techno... » read more

Multi-Core Architecture Optimized For Time-Predictable Neural Network Inference (FZI, KIT)

By Technical Paper Link - 21 Nov, 2025 - Comments: 0

A new technical paper titled "MultiVic: A Time-Predictable RISC-V Multi-Core Processor Optimized for Neural Network Inference" was published by researchers at FZI Research Center for Information Technology and Karlsruhe Institute for Information Technology (KIT). Abstract: "Real-time systems, particularly those used in domains like automated driving, are increasingly adopting neural network... » read more

Classical Computing vs. Machine Learning and Edge AI Techniques in Various Application Domains

By Infineon - 06 Nov, 2025 - Comments: 0

Machine Learning (ML) algorithms have revolutionized various domains by enabling data-driven decision-making and automation. The deployment of ML models on embedded edge devices, characterized by their constrained computational resources and low power requirements, presents unique challenges and opportunities. As the digital world continues to generate increasingly complex and high-volume da... » read more

AI-Empowered Analog IC Sizing Methods (Univ. of Glasgow Et Al.)

By Technical Paper Link - 10 Oct, 2025 - Comments: 0

A new technical paper titled "From Systematic to Intelligent: Assessing AI-Empowered Optimization Techniques for Analog Building Block Sizing" was published by researchers at University of Glasgow, Mediatek, The University of Edinburgh, Magics Technologies NV, University of Sevilla and Georgia Institute of Technology. Abstract "This paper presents a comprehensive, design-insight-based compa... » read more

What Does Semiconductor Disruption Look Like?

By Brian Bailey - 25 Sep, 2025 - Comments: 2

When conducting interviews for my article on the incorporation of AI within EDA tools, Anand Thiruvengadam, senior director and head of AI product management at Synopsys, said, "AI has the potential to transform how customers do chip design. The entire EDA flow can be disrupted with AI." He is not alone in making this kind of statement. Each year, I do a predictions piece, and I ask about how A... » read more

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tag: machine learning

Force Fields Will Accelerate Atomistic Simulations By 10,000× In 2026, Unlocking New Era Of Discovery

HW-Triggered Backdoors Across Common GPU Accelerators (BIFOLD, TU Berlin, CISPA)

Accelerating Semiconductor Innovation Through Machine Learning-Driven Modeling

Benefits And Limits Of Using ML For Materials Discovery

AI Techniques To Solve HW-SW Challenges For Useful Quantum Computing (Nvidia, U. of Oxford et al.)

AI With Open And Scaled Data Sharing in IC Manufacturing (NIST)

Multi-Core Architecture Optimized For Time-Predictable Neural Network Inference (FZI, KIT)

Classical Computing vs. Machine Learning and Edge AI Techniques in Various Application Domains

AI-Empowered Analog IC Sizing Methods (Univ. of Glasgow Et Al.)

What Does Semiconductor Disruption Look Like?

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