Semiconductors for edge computing – deployable compute nodes closer to data sources to reduce latency. Edge chips include efficient AI accelerators, ruggedized processors, and network/connectivity chips that handle workloads on-site (factories, vehicles, remote sensors) rather than in cloud data centers. Emphasis is on low power, often real-time processing, and sometimes domain-specific acceleration (for vision analytics, etc.).
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Nordic expands its ultra-low-power cellular IoT portfolio with Cat 1 bis, satellite NTN, and advanced LTE-M/NB-IoT with edge AI. The post Nordic debuts multiple cellular IoT products appeared first on EDN.
TI’s MSPM0G5187 and AM13E23019 MCUs integrate the TinyEngine NPU, enabling efficient edge AI in systems ranging from simple to complex. The post TinyEngine NPU powers AI in TI MCUs appeared first on EDN.
The i.MX 93W applications processor from NXP combines a dedicated AI NPU with secure tri-radio wireless connectivity in a single package. The post Edge AI SoC integrates tri-radio appeared first on EDN.
Every generation of mobile networks, from 1G to 5G, has rewritten the rules of how the world lives and works. The coming 6G revolution, by decade’s end, will represent a new direction still, toward a universal data fabric where millions of agents collaborate in real-time across the digital and physical...
CHESEAUX-SUR-LAUSANNE, Switzerland, and PHOENIX (AZ), USA — March 10, 2026 — Kudelski Labs, the innovation arm of the Kudelski Group (SIX:KUD.S) and a global leader in embedded security for semiconductors and connected devices, today announced that Axelera AI has integrated the Kudelski Secure Enclave, robust to AVA_VAN.3 (“KSE3”) into Europa, its new high-performance, Edge AI processing platform. The collaboration strengthens the security foundation of Europa, ensuring that...
Announced today at Embedded World, the new Core Series 2 may accelerate the industrial edge and real-time applications, while the healthcare suite may help providers meet the challenges of patient care using edge AI.
Architecting solutions for edge AI is not about minimizing cloud solutions or making small extensions of existing MCUs/MPUs. It's a hardware/software/model co-development problem. The post AI Power on the Edge appeared first on Semiconductor Engineering.
Edge AI, robotics, and digital twins are converging to enable physical AI-driven manufacturing systems. The post From DX to AX: AW 2026 Signals the Rise of Autonomous Manufacturing appeared first on EE Times.
The Sensors Converge Conference is one of the premier technical gatherings dedicated to the design, integration, and deployment of sensing technologies across industries. The event brings together engineers, system architects, researchers, and product developers to explore advancements in sensor hardware, edge computing,… Read More The post Sensors Converge: Where Intelligence...
These devices are redefining what an MCU can do in the age of edge AI, advanced connectivity, and software-defined vehicles.