#forlinxembedded

🚀 Unlocking Microsecond-Level Real-Time Control in Industrial Automation 🚀

In the world of industrial automation, achieving microsecond-level jitter control for high-precision applications like robotics and multi-axis motion control has always been a challenge. But with the combination of IgH EtherCAT Master and the Preempt-RT real-time kernel running on the Forlinx Embedded OK3576-C development board, we can now push the limits of real-time communication and control!

🔧 Key Highlights:

⚫ Achieved exceptional real-time performance with microsecond-level jitter reduction.

⚫ Cycle Synchronous Velocity Mode (CSV) reduced jitter from 6.308 μs to 3.579 μs.

⚫ Cycle Synchronous Torque Mode (CST) reduced jitter from 50.047 μs to 2.113 μs under full load stress.

⚫ CPU isolation strategy applied for even better performance.

This groundbreaking solution opens up new possibilities for industries that require precise control and reliability.

Forlinx Embedded 2025 Recap: Powering the Next Wave of Industrial Edge AI Intelligence

2025 marked a year of deep collaboration, platform innovation, and real-world deployment for Forlinx Embedded.

As an NXP Gold Partner and official i.MX95 ecosystem partner, we continued aligning closely with NXP’s roadmap for secure, safety-ready edge computing—while further strengthening our strategic partnerships with Rockchip and Allwinner to deliver scalable, cost-effective AI edge platforms.

Throughout the year, we introduced a new generation of industrial System on Modules (SoMs), SBCs, and fanless embedded computers, supporting applications from safety-critical automation and smart energy to machine vision, transportation, and IoT gateways.

Across all platforms, three principles remain consistent: 🔹 Industrial-grade reliability over time 🔹 Predictable system behavior for certification and deployment 🔹 A smooth path from evaluation to mass production, with 10–15 years lifecycle support

From i.MX95 high-end edge controllers to RK3588 AI edge computers and ultra-low-power industrial control SoMs, we are bringing AI closer to where data is generated—securely, reliably, and at scale.

Looking ahead, we remain committed to building long-life embedded platforms that reduce integration risk and accelerate industrial AI adoption worldwide.

🚦 When traffic demand changes in seconds, lane control should too.

Morning rush hour.

Left-turn lanes backed up for hundreds of meters — while straight lanes sit nearly empty.

Yet traditional flexible lane systems still rely on fixed schedules or cloud-only control.

🔍 The problem?

Long perception-to-decision cycles and network latency.

💡 The solution: Edge intelligence at intersections.

With the FCU3501 AI Edge Computing Box, built on the industrial-grade RK3588J, traffic perception and decision-making move directly to the roadside:

☑ Real-time AI video analysis

☑ Local, autonomous lane control decisions

☑ Reduced cloud dependency and network latency

☑ 24/7 reliable operation in harsh outdoor environments

From timed to real-time,

from centralized to distributed,

from preset to autonomous.

🚀 Ensuring Reliability in Extreme Conditions

From factory to field, embedded development boards face harsh environments—from truck jolts to high-frequency device vibrations. At Forlinx Embedded, we perform rigorous vibration and shock tests, simulating frequencies from 12Hz to 2000Hz and accelerations up to 100g, ensuring our boards remain stable and reliable in industrial and transportation applications.

Products like the OK3506J-C and OK3588-C undergo these "brutal tests" to identify potential weaknesses before deployment, reducing on-site failures and ensuring long-term performance.

🚀 Ensuring Reliability in Extreme Conditions

From factory to field, embedded development boards face harsh environments—from truck jolts to high-frequency device vibrations. At Forlinx Embedded, we perform rigorous vibration and shock tests, simulating frequencies from 12Hz to 2000Hz and accelerations up to 100g, ensuring our boards remain stable and reliable in industrial and transportation applications.

Products like the OK3506J-C and OK3588-C undergo these "brutal tests" to identify potential weaknesses before deployment, reducing on-site failures and ensuring long-term performance.

Complete Troubleshooting Guide for AM62x Development Boards — Phase 1 & 2

The TI AM62x processor is widely used in industrial control, HMI, and edge computing applications.

During development, engineers often face interface-related issues that can slow progress — from startup failures to communication mismatches.

To help developers debug faster and design more confidently, Forlinx Embedded has published a two-part guide:

📘 Phase 1:

General debugging framework and troubleshooting ideas for:

✅ Power, reset, and clock signal checks

✅ System startup failures

✅ I²C, SPI, USB, and SDIO communication issues

→ Includes practical tests like cross-board validation, impedance tuning, and signal measurement.

📗 Phase 2:

Advanced interface troubleshooting, including:

✅ LVDS display mismatches (VESA/JEIDA mode, soft-start circuit)

✅ Ethernet (ENET) PHY/MDIO configuration and equal-length design

✅ CAN / RS485 / UART communication errors

✅ Audio, PCIe, GPIO stability and compatibility issues

🔧 FET153-S SoM: Multi-core Power for Next-Level PLC Control

In the era of Industry 4.0, Programmable Logic Controllers (PLCs) face increasing demands for real-time control, high-throughput data handling, and industrial-grade reliability.

The FET153-S SoM, powered by the Allwinner T153 processor, combines a 4-core Cortex-A7 for high-performance computing with an independent RISC-V E907 for real-time tasks. This multi-core heterogeneous architecture resolves the classic PLC challenge: balancing fast computation with precise control.

🚀 Deploying MobileNetV3 on the OKMX8MP-C for Edge AI

In our latest technical case, Forlinx Embedded demonstrates how to train, quantize, and deploy the MobileNetV3 model using NXP’s eIQ Portal, achieving handwritten digit recognition on the OKMX8MP-C (i.MX8M Plus) platform.

This end-to-end workflow covers everything — from dataset import and model training to TensorFlow Lite quantization and deployment — showcasing how developers can efficiently implement lightweight AI inference on industrial ARM platforms.

✅ Tools used: eIQ Portal + TensorFlow Lite ✅ Platform: OKMX8MP-C SoM (NXP i.MX8M Plus) ✅ Application: Handwritten digit recognition