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Essential CRA Compliance！Forlinx Embedded Achieves Dual IEC 62443 Certifications, Paving a Secure Path for Global Expansion
Forlinx Embedded achieves dual IEC 62443 certifications. Secure your industrial systems and ensure EU CRA compliance for successful global market expansion.
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OK3562 Buildroot: Two Methods to Add User Files to the Image
Learn how to integrate files into the RK3562 Linux image using Buildroot fs-overlay and rootfs mounting. Optimize your OK3562 development workflow today.
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Porting Mobilenet_V3 Model for Handwritten Digit Recognition on OKMX8MP Linux 5.4.70
Step-by-step guide to training, quantizing, and deploying MobileNet_V3 on OKMX8MP (i.MX8M Plus) using MNIST and TensorFlow Lite under Linux 5.4.70.
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Optimizing Boot Time: How to Shorten Startup Duration on T536 (Linux 5.10.198)
Learn how to reduce the T536 platform boot time from 14.5s to 7s through log level adjustments, U-Boot menu disabling, and kernel fast boot optimizations for Linux 5.10.
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How to Enable Core Dump for Debugging on RK3568 with Buildroot (Linux 5.10 Kernel)
Learn to enable Core Dumps on OK3568 Buildroot systems. Fix segmentation faults and stack overflows using GDB for efficient embedded Linux application debugging.
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Forlinx FCU2601 Obtains EN 18031 Certification for Global Energy Storage Markets
Forlinx FCU2601 passes EN 18031 cybersecurity certification by Bureau Veritas, ensuring high-level protection for global energy storage and industrial EMS applications.
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Forlinx Embedded Honored with "2025 Annual Outstanding Contribution Award"
Forlinx Embedded honored with Rockchip's 2025 Annual Outstanding Contribution Award for achievements in product innovation and strategic collaboration.
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Real-Time Control on Linux: Preempt-RT + IgH EtherCAT Master on OK3576-C
Achieve microsecond-level jitter control and high-performance real-time communication with IgH EtherCAT Master and Preempt-RT on the Forlinx OK3576-C.
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OK3568 Platform Electric Bicycle Recognition Solution: From Model Training to Hardware Deployment
Learn how to train a custom YOLOv5 model, convert .pt to RKNN, and deploy on RK3568. Covers environment setup, LabelImg, ONNX export, and rknpu2 runtime deployment.
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How to Deploy YOLOv8 on OK3576 for Camera-Based Object Detection
Learn how to convert YOLOv8 models to RKNN and deploy on OK3576-C with RK3576 NPU for real-time embedded AI object detection.
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