
Efficient, low-power vision AI microprocessors enable real-time inference at the edge for robotics, surveillance, and smart home devices.

A multicore processor, development board, and software combine to speed the design of edge AI applications.

Platform providers, device manufacturers, and semiconductor companies like Silicon Labs converge around the Matter protocol for smart device interconnectivity.

Learn how to use edge AI to extend battery life in wireless motor-monitoring applications.

Edge AI and microcontrollers enable efficient, scalable AI processing, reducing costs and power consumption while optimizing real-time applications.

Discover the Raspberry Pi 500, a powerful all-in-one computer in a keyboard. Explore specs, GPIO upgrades, and its perfect pair—the Pi Monitor.

Learn how Panasonic's hybrid capacitors, precision resistors, and wireless modules address key power management challenges in AI datacenters.



Use ultra-low-power MCUs with specialized capabilities to meet application requirements without compromising processor performance or power budgets.

Learn how the ADI 78002 MCU can support the design of edge AI applications with low power consumption, high performance, and robust security features.

Digit's AI-powered brain enables local real-time voice conversations using Hopper Chat and Llama 3. Learn how to set it up with NVIDIA Jetson Orin Nano.

Discover how Hacksmith Industries built the ultimate autonomous security drone system at H.E.R.C., featuring drones, robot dogs, and AI-driven turrets.

iWave introduces the iW-RainboW-G58M System on Module (SoM), powered by the Intel Agilex™ 5 FPGA, the first FPGA to feature integrated AI capabilities.

Learn about the application of linear control models and PCA classification in creating a voice-controlled robot.

The BeagleY-AI is here! Featuring a Texas Instruments AM67A processor and 4 TOPS of Edge AI acceleration, it's perfect for advanced AI applications.

Explore the vast ESP32 ecosystem with this overview, featuring detailed explanations of its architecture, communication interfaces, and development tools.

Learn how to prepare data for machine learning projects. Explore techniques for scaling features, handling missing values, and ensuring fair & accurate results.