A multi-tier approach to machine learning at the edge
A multi-tier approach to machine learning at the edge can help streamline both development and deployment for the AIoT.
MCU suppliers increase memory content to support AIoT designs
High-resolution displays and complex AI datasets in IoT endpoints and wearable designs increasingly demand additional memory content.
AI of Things (AIoT) explained
A look at the growing trend for intelligent connected devices enabling the AI of Things (AIoT), the technologies and some…
SOM accelerates deployment of edge-based applications
The Kria K26 adaptive system-on-module (SOM) from Xilinx is a production-ready embedded board for rapid deployment of edge-based applications.
AI-based automatic visual packaging control
Here is how AI can be used to quickly and effectively identify and eliminate complex packaging quality problems in production…
Development of CMOS image sensors accelerates to serve a variety of applications
The design of CMOS image sensors is going through an accelerated phase of development to serve a variety of applications.
AI at the edge could provide solutions for IoT security issues
AIoT seems to be here to stay, and the next development step—as with processing—will be to evolve specialized devices for…
Renesas MPUs Tout High-Precision AI Accelerator
Featuring Renesas Electronics' exclusive AI accelerator DRP-AI, the new RZ/V2L MPUs make embedded AI easier and more power efficient.
Better Memristors Improve AI Operations
Neurone-like junctions made of mixed oxide-based materials could reduce the massive energy consumption of artificial intelligence operations.
Processor architecture brings AI to IoT
Arm introduced a processor architecture that aims to meet the growing need for both enhanced security and AI functionality at…