Investors kept pouring money into AI hardware startups in the second quarter of 2026. While companies focused on chips for AI data centers have largely dominated the funding over the past year, ...

Multiomics data integration with machine learning has become the standard approach for combining genomic, transcriptomic, proteomic, and metabolomic measurements collected from the same biological ...

In this tutorial, we delve into CuPy as a powerful GPU-accelerated alternative to NumPy for high-performance numerical computing in Python. We start by inspecting the available CUDA device, checking ...

A chip designed to convert high voltages into lower levels in electronics — a process known as DC-DC step-down conversion — more efficiently using a piezoelectric resonator. Photos by David Baillot/UC ...

Integrating AI into chip workflows is pushing companies to overhaul their data management strategies, shifting from passive storage to active, structured, and machine-readable systems. As training and ...

In industrial recommendation systems, the shift toward Generative Retrieval (GR) is replacing traditional embedding-based nearest neighbor search with Large Language Models (LLMs). These models ...

They used this system to design complex silicon structures, each roughly the same size as a dust particle, that can perform computations using heat conduction. This is a form of analog computing, in ...

Abstract: To solve manual reliance, poor noise resistance and low robustness in industrial weld defect detection, an intelligent system based on sparse dictionary ...

Update 2025.11.27: Major refactoring into modular architecture (Module A/B/C plus unified interface) with comprehensive benchmark suite. Update 2025.06.25: Added PyAMGX support with improved ...