#embeddedai

[julessmith11]

Embedded AI in Martech: The Future of Intelligent Marketing

Artificial Intelligence is changing the future of digital marketing, and embedded AI in Martech is helping businesses automate smarter and faster. Companies across the USA are now using AI-powered marketing technologies to improve customer engagement, personalize campaigns, and increase overall marketing performance.

Embedded AI enables marketers to analyze customer behavior, automate workflows, and deliver real-time personalized experiences. From predictive analytics to intelligent customer segmentation, AI-driven Martech solutions are helping businesses make data-driven decisions with greater efficiency.

As digital competition continues to grow, businesses are adopting AI-enabled marketing strategies to improve ROI and streamline operations. Embedded AI is becoming an essential part of modern marketing technology for brands looking to scale successfully.

Read the full blog here: https://www.truperformance.us/resources/blogs/embedded-ai-in-martech/

Explore more insights from Tru Performance:

• Generative Engine Optimization Guide https://www.truperformance.us/resources/blogs/generative-engine-optimization-guide/

• Visit Tru Performance https://www.truperformance.us/

[brainyneurals]

Edge AI Development: On-Device Solutions for Privacy

Discover how Brainy Neurals leverages Embedded edge AI development to deliver faster, secure, and privacy-first AI solutions. By processing data directly on devices, businesses gain real-time insights, reduced latency, and stronger data protection ideal for healthcare, IoT, and smart systems. Build intelligent, future-ready applications today.

Discover how embedded edge AI development enables secure, real-time data processing on devices. Learn benefits, use cases, tools, and why bu

[timestechnow]

Microchip Unveils Production-Ready Full-Stack Edge AI for MCUs and MPUs

Microchip Technology has extended its edge AI offering with full-stack solutions that streamline development of production-ready application

Microchip Technology Inc. unveils production-ready full-stack Edge AI solutions for MCUs and MPUs — empowering developers with integrated, scalable tools to accelerate intelligent applications at the edge.

[forlinx]

Learn how to train a custom YOLOv5 model, convert .pt to RKNN, and deploy on RK3568. Covers environment setup, LabelImg, ONNX export, and rk

The journey from a local training environment to real-time inference on embedded hardware can be a complex puzzle. I’ve just documented a complete pipeline for training a custom YOLOv5 model and successfully deploying it on the Rockchip RK3568 NPU.

The Workflow Highlights:

🛠️ Environment Setup: Navigating the specific Python 3.8/3.9 requirements for YOLOv5 and LabelImg on Windows 11. 🏷️ Dataset Engineering: Implementing a clean directory structure and YOLO-format annotation for high-quality training. 🔄 Model Transformation: Converting the PyTorch .pt weights into .onnx and finally into .rknn using the RKNN-Toolkit2 on Ubuntu 22.04. 🚀 Hardware Deployment: Compiling the C++ RKNPU2 demo and optimizing the post-processing headers for high-performance edge inference.

The result? A seamless transition from a standard PC environment to efficient, hardware-accelerated detection on an embedded development board.

Check out the full breakdown below! 👇 https://www.forlinx.net/industrial-news/yolov5-training-rk3568-rknn-deployment-guide-775.html

[forlinx]

Learn how to convert YOLOv8 models to RKNN and deploy on OK3576-C with RK3576 NPU for real-time embedded AI object detection.

🚀 Edge AI Made Easy with OK3576-C & YOLOv8!

With the rapid growth of intelligent vision applications, real-time on-device object detection has become a core capability for industrial vision, smart devices, and surveillance.

At Forlinx Embedded, we’ve demonstrated a full workflow from model to edge deployment using the OK3576-C board with RK3576 NPU:

1️⃣ Convert YOLOv8 pre-trained models (.pt) → RKNN format 2️⃣ Deploy the model on the OK3576-C board 3️⃣ Run real-time object detection using a USB camera (~30 FPS)

This guide helps developers quickly move from model training to edge inference with minimal setup, leveraging the RK3576 NPU’s efficiency and power.

[timestechnow]

congatec COMs Fast-Track Intel Core Ultra Series 3 for Embedded AI Without Discrete Accelerators

congatec – a leading vendor of embedded and edge computing building blocks launched the industry’s broadest Intel Core Ultra Series 3.

congatec COMs fast-track Intel Core Ultra Series 3 for embedded AI without discrete accelerators, enabling powerful AI performance directly on board and simplifying design complexity for next-gen embedded systems.

[timestechnow]

AAEON Announces Jetson T4000-Based Embedded AI Box PC

Following the announcement of NVIDIA Jetson T4000 module at CES, industry-leading provider of edge AI solutions AAEON.

AAEON announces Jetson T4000-based embedded AI Box PC, delivering powerful on-edge AI performance and robust processing for intelligent automation, robotics, and industrial AI applications.

[timestechnow]

LLM Acceleration on Versal AI Edge: Brought to you by RaiderChip and iWave

As GenAI continues to revolutionize industries, the demand for efficient, high-performance computing platforms has RaiderChip.

RaiderChip and iWave are partnering to bring game-changing Generative AI capabilities to the edge with their collaboration featuring the Versal AI Edge VE2302 System-on-Module. Designed to accelerate large-language models (LLMs) in compact, low-latency environments.