This toolkit enables the deployment of AI models on Rockchip NPUs, targeting developers and researchers working with embedded systems and edge AI. It facilitates model conversion, inference, and performance evaluation, accelerating AI application implementation on Rockchip hardware.

How It Works

The RKNN-Toolkit2 operates by converting trained AI models into an RKNN format. This proprietary format is then optimized for Rockchip's Neural Processing Units (NPUs). Users interact with the toolkit via Python or C APIs on the development board for inference, while the RKNPU kernel driver handles direct hardware interaction. This approach allows for efficient execution of AI workloads on Rockchip's specialized hardware.

Quick Start & Requirements

• Install: pip install rknn-toolkit2
• Prerequisites: Python 3.6-3.12.
• Resources: Download SDK, Docker images, examples, and platform-tools from RKNPU2_SDK.
• Docs: https://github.com/airockchip/rknn-toolkit2

Highlighted Details

• Supports a wide range of Rockchip platforms including RK3588, RK3576, RK3566/RK3568, RV1103/RV1106, and RV1126B.
• v2.3.2 includes support for RV1126B, improved einsum and Norm operations, automatic mixed precision, and enhanced graph optimization.
• Not compatible with the older RKNN-Toolkit.
• RKNN-LLM SDK available for Large Language Model deployment.

Maintenance & Community

• Active development with recent updates (v2.3.2).
• Community support via QQ groups (e.g., 958083853).
• Feedback recommended via Redmine (requires account).

Licensing & Compatibility

• License details are not explicitly stated in the README.
• Compatibility for commercial use or closed-source linking is not specified.

Limitations & Caveats

The README does not specify the license, making commercial use and closed-source integration uncertain. Support for older Rockchip platforms (RK1808, RV1109/RV1126, RK3399Pro) requires using the legacy rknn-toolkit repository.