#embeddedai

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/

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.

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.

🚀 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)