Description

RKNN software stack can help users to quickly deploy AI models to Rockchip chips. The overall framework is as follows:

In order to use RKNPU, users need to first run the RKNN-Toolkit2 tool on the computer, convert the trained model into an RKNN format model, and then inference on the development board using the RKNN C API or Python API.

• RKNN-Toolkit2 is a software development kit for users to perform model conversion, inference and performance evaluation on PC and Rockchip NPU platforms.

• RKNN-Toolkit-Lite2 provides Python programming interfaces for Rockchip NPU platform to help users deploy RKNN models and accelerate the implementation of AI applications.

• RKNN Runtime provides C/C++ programming interfaces for Rockchip NPU platform to help users deploy RKNN models and accelerate the implementation of AI applications.

• RKNPU kernel driver is responsible for interacting with NPU hardware. It has been open source and can be found in the Rockchip kernel code.

Support Platform

• RK3588 Series
• RK3576 Series
• RK3566/RK3568 Series
• RK3562 Series
• RV1103/RV1106
• RV1103B/RV1106B
• RK2118

Note:

​ For RK1808/RV1109/RV1126/RK3399Pro, please refer to :

​ https://github.com/airockchip/rknn-toolkit

​ https://github.com/airockchip/rknpu

​ https://github.com/airockchip/RK3399Pro_npu

Download

• You can also download all packages, docker image, examples, docs and platform-tools from RKNPU2_SDK, fetch code: rknn
• You can get more examples from rknn mode zoo

Notes

• RKNN-Toolkit2 is not compatible with RKNN-Toolkit
• The supported Python versions are:
  • Python 3.6
  • Python 3.7
  • Python 3.8
  • Python 3.9
  • Python 3.10
  • Python 3.11
  • Python 3.12
• Latest version:v2.3.0

RKNN LLM

If you want to deploy LLM (Large Language Model), we have introduced a new SDK called RKNN-LLM. For details, please refer to:

https://github.com/airockchip/rknn-llm

CHANGELOG

v2.3.0

• RKNN-Toolkit2 support ARM64 architecture

• RKNN-Toolkit-Lite2 support installation via pip

• Add support for W4A16 symmetric quantization (RK3576)

• Operator optimization, such as LayerNorm, LSTM, Transpose, MatMul, etc.

for older version, please refer CHANGELOG

Feedback and Community Support

• Redmine (Feedback recommended, Please consult our sales or FAE for the redmine account)
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