AI on the Edge

Advanced real-time microcontrollers integrate AI functionality to enable seamless digital power management and motor control in automotive and industrial applications.

The Synaptics Astra platform simplifies edge AI deployment by combining scalable hardware, unified software, and open-source AI.

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.

These ruggedized AI and IIoT computer systems encompass everything from modular designs to compact solutions that can handle everything harsh environments throw at them.

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.

Gain insight into the development boards leading the way in driving innovation as AI technologies ramp up across the industry.

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....

Discover how SBCs with integrated AI processing power are bringing new capabilities to many industries.

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-...

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...

Femtosense CEO Sam Fok weighs in on the challenges and benefits of implementing sparse AI for real-time edge applications.

Untether AI’s Robert Beachler discusses the compute bottleneck in today’s AI workloads, and how his company’s at-memory compute architecture tackles the problem.

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.

This application example uses the B-U585I-IOT02A Discovery Kit, STM32Cube.AI Developer Cloud, and AWS’ solutions to run an audio classification model on an STM32U5 MCU.

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.

Offering protection against high voltage and noise interference, Analog Devices' iCoupler isolation technology can create edge AI systems for harsh and high-voltage environments...

NanoEdge AI Studio from STMicroelectronics is an ML technology that speeds innovation, able to create an optimal ML library for an AI project based on a minimal amount of data...

PSOC Edge opens the door to a new generation of responsive machine-learning-based, user-centric edge devices.

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.

Sponsored by Texas Instruments: Embedded processors capable of running AI algorithms coupled with “low-code” software-development tools lead to edge AI applications that can boost...

Standalone AI on the edge is possible with Useful Sensors’ modules.

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...

Configurable high-bandwidth RISC-V cores with vector units can be made to directly address challenging applications like machine learning, AI, and other cutting-edge spaces.

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.

Femtosense's SPU-001 coprocessor delivers low-power AI audio processing.

A novel edge AI video analytics platform built by Elma Electronic, Hailo, and Isarsoft challenges how business intelligence is addressed in the transportation industry.

The Cadence Tensilica AI Platform delivers scalable and energy-efficient on-device to edge AI processing.

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.

The machine-learning support from Synaptics' Katana SoC can visually detect actions like falling.

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...

NXP's Robert Oshana discusses issues regarding machine learning at the edge.

Today's advanced products, from consumer wearables to smart EVs, are starting to leverage the power of AI to increase performance and functionality. However, those solutions require...

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.

Our panel addressing the use of artificial intelligence in the field includes experts from NVIDIA, Lattice Semiconductor, and NXP.

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.

Renesas’ DRP is designed to handle chores ranging from machine learning to stream processing.

Inference arrives at the edge: The eIQ machine-learning framework and development tools facilitate the transfer of AI from the cloud to devices on the edge.

Sponsored by Digi-Key and NXP Semiconductors: Designs evolve over time, but instead of jumping to a new platform and ecosystem to meet new requirements, learn one at the start...