AI on the Edge

Right-sizing hardware to balance performance, power consumption, and cost enables machine learning to run on resource-constrained platforms.

The increasing demands for generative AI at the edge call for new solutions that can achieve high performance within the low-power requirements for edge apps.

AI integration within robots changes everything about workflows, but it requires workforce retraining to mitigate job replacements.

The Raspberry Pi AI Kit comes with an M.2 HAT+ and Hailo AI acceleration module capable of pushing 13 TOPS.

AI infusion amplifies engineer’s abilities and the systems capabilities as well as shortens the development time. Faster results, easier design, and flawless code is possible....

The digital-twin platform, Earth-2, uses AI and machine-learning models for improved weather predictions with enhanced visualizations.

Transforming "smart" devices into "intelligent" components via AI requires more than computational power. It will be enabled by other technologies, most of which rely on new materials...

AI is transforming semiconductor design by automating tedious tasks, enhancing creativity, and enabling innovative chip architectures, all while addressing the growing workforce...

Embedded-systems designers are on a mission to squeeze powerful AI algorithms into resource-constrained gadgets, relying on cutting-edge custom hardware accelerators and high-...

Particle’s new 5G-capable Tachyon single-board computer brings AI acceleration to spark innovation and drive embedded and AIoT applications.

Although artificial intelligence is a significant technology advance, it also serves as a reminder of how technology disruption has impacted society in the past.

Artificial-intelligence algorithms, particularly artificial neural networks and their hardware, are critical in addressing edge AI applications.

Analog designs are challenging, but artificial-intelligence optimizations are improving and speeding up the design process.

The convergence of high-powered edge AI and smarter video cameras revolutionizes vision systems in their delivery of real-time awareness and responses.

Intelligence and the edge—what could possibly go wrong? Plenty, it turns out, whether it’s power limitations or security concerns, among other issues.

Infineon’s PSoC Edge adds machine-learning support to its configurable microcontroller.

Code reduction and simple verification are just two of the benefits that high-level synthesis brings to AI acceleration.

Artificial intelligence belongs on the edge, where it can do things that are beyond the scope of people.

The new processor family from Texas Instruments can handle vision and AI processing for up to 12 cameras.

Embedded smart-camera platforms with easy-to-use edge learning provide a unique solution for apps that are too difficult for conventional rule-based machine vision but don’t justify...

Explainable AI (XAI) can improve safety, security, and the overall user experience in IoT applications.

Elma’s JetSys-5330 is built around NVIDIA’s Jetson AGX Orin.

Highlights of DFI's exhibition include high-performance industrial motherboards featuring AMD's 1.8-in. Ryzen, Intel's newest Raptor Lake-S and Alder Lake series processors, and...

Artificial intelligence and more specifically, machine learning, with human augmentation requires a thorough strategy to achieve a breakthrough.

Artificial intelligence is furthering its way into everyday lives, but the focus has predominantly been on the “big machines,” such as self-driving cars. Perhaps it’s now time...

Some results of our recent machine-learning survey were expected, but others raised eyebrows.

Edge AI tools continue to improve and become more powerful, leading the way to a new era of intelligent video analytics with rapidly expanding capabilities and possible uses.

With all the buzz surrounding edge computing these days, perhaps you're thinking it’s time to invest in intelligent edge technologies for your IoT network. What are the key factors...

The Jetson family of modules can find homes in a wide variety of systems, bringing low-power machine learning to embedded applications.

AI at the edge is growing rapidly. Don’t let these misguided notions get in your way.