Building a Custom AI Vision Platform with Tria IQ9000-Series – From Board to Box

Introduction: Bringing Intelligence to the Edge

When performance, energy-efficiency, and scalability meet real-world design, the result is a new generation of Vision-AI-ready systems that can adapt to almost any industrial or robotics challenge.

Tria Technologies’ IQ9000-series (based on Qualcomm IQ-9075M SIP) is designed exactly for that. Delivering 100 TOPS of AI compute in a 3.5-inch industrial SBC form factor, it enables OEMs to bring AI-driven intelligence into demanding edge environments — from robotic arms and industrial gateways to autonomous inspection systems.

Custom Use Case: The Vision AI Edge-Box

For one of Tria’s industrial automation partners, the standard development kit was just the beginning. The team needed a production-ready device that could be deployed across factory lines for AI-based quality inspection, object detection, and predictive maintenance.

Hardware Customization

The project started from the reference Vision AI-Kit IQ9 SBC platform and evolved into a compact, ruggedized Edge-Box IQ9:

• Custom motherboard design integrated field-specific I/O — including isolated GPIO, additional RS-485, and dual 2.5 Gb TSN Ethernet.

• AI-tuned power architecture allowed dynamic scaling between 15 W and 45 W operation.

• Thermal optimization used a CNC-machined aluminum enclosure with active-cooler mounting directly on the IQ-9075M SIP for -40 °C to +85 °C conditions.

• Modular expansion through 3 × M.2 slots for NVMe storage, Wi-Fi 6E, and 5G cellular connectivity

Software Enablement

Running Qualcomm Linux 1.5 (kernel 6.6.90) with QIRP SDK, the system leveraged Qualcomm’s full AI-software stack:

• Dual HTP AI engines (100 TOPS) accelerated TensorFlow Lite, ONNX, and Qualcomm AI Engine Direct models.

• ROS 2 (Jazzy) integration powered real-time robotics communication and motion control.

• Edge Impulse workflows enabled model training and deployment directly onto the device.

• Foundries.io managed over-the-air updates and secure containerized maintenance.

The customer’s vision models — developed using Qualcomm AI Hub and Edge Impulse Studio — were optimized for real-time detection on nine concurrent camera streams, all composited via the Adreno 765 GPU into a 3 × 3 video wall on dual 4K displays.

Qualcomm Software Center

From Prototype to Production

Using Tria’s mechanical engineering team, the kit evolved into a fully integrated product:

• Custom sheet-metal and machined enclosure with front-mount camera interfaces.

• Thermal chamber validation and EMC pre-certification for industrial standards.

• Seamless transition from Tria’s Vision AI-Kit prototype to mass-production assembly, all supported within the Tria–Avnet ecosystem.

The final Edge-Box IQ9 was capable of:

• Processing up to 32 HD video streams with real-time AI inference.

• Delivering remote OTA management and predictive analytics through AWS IoT Connect.

• Operating continuously in heavy industrial and outdoor environments.

Outcome and Impact

This IQ9000-based custom design reduced inference latency by 70 %, improved accuracy through multi-camera fusion, and cut system power consumption by 40 % compared with GPU-based alternatives.

With long-term component availability and industrial-grade reliability, the customer gained a future-proof AI platform ready for expansion into multiple machine-vision applications.

Conclusion

The IQ9000 Vision AI Edge-Box highlights how Tria Technologies combines compute module expertise, mechanical customization, and software enablement into complete OEM-ready solutions.

From SMARC and OSM modules to custom enclosures and full systems, Tria helps customers shorten development cycles, lower risk, and scale Edge-AI innovation.

Tria Technologies Systems