As AI applications increasingly demand real-time processing on the edge, the choice of SoC (System-on-Chip) becomes crucial. Rockchip’s RK3576 is emerging as a strong contender for edge AI inference thanks to its integrated NPU, powerful CPU/GPU cores, and rich peripheral support.

Why RK3576?

The RK3576 features:
• Octa-core CPU: 4x Cortex-A72 + 4x Cortex-A53 (big.LITTLE architecture)
• Embedded 6 TOPS NPU: Supports mainstream AI frameworks such as TensorFlow Lite, ONNX, and Caffe via RKNN
• Mali-G610 GPU: Ideal for AI+graphics use cases like AR/VR and smart UI
• Support for multiple camera inputs, MIPI-CSI, HDMI, LVDS, and eDP
• Dual-core ISP for advanced image processing and face detection

Real-World Use Cases
• Smart Surveillance: Real-time object detection, face recognition, license plate recognition
• Retail Automation: Smart checkout, customer behavior analysis, shelf detection
• Industrial IoT: Edge defect detection, worker safety monitoring, equipment status diagnostics
• AIOT Devices: Smart door locks, AI terminals, voice control panels

AI Inference with RKNN Toolkit

Using Rockchip’s RKNN Toolkit, developers can:
• Quantize and convert models to run efficiently on the embedded NPU
• Benchmark model performance directly on the board
• Optimize memory usage and execution speed for real-time applications

✅ Models tested: YOLOv5s, MobileNetV3, EfficientNet, NanoDet

Our Experience

At Rocktech, we provide customizable SBCs based on RK3576 tailored for your application. We help clients integrate dual cameras, touch panels, sensors, and connectivity modules while optimizing power and cost.

Get Started

If you are building an AI product and looking for a powerful edge inference platform, RK3576 offers a balanced architecture between AI compute, power efficiency, and rich I/O.

🧠 Need help? We offer design-to-production support, including SDK integration, driver adaptation, and UI development.

👉 Contact us to custom your SBC board