This repository is no longer maintained and has been moved to https://github.com/airockchip/rknn-toolkit2/ . 本仓库不再维护，已经移到https://github.com/airockchip/rknn-toolkit2 。
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.
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RKNN-Toolkit2 is a software development kit for users to perform model conversion, inference and performance evaluation on PC and Rockchip NPU platforms.
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RKNN-Toolkit-Lite2 provides Python programming interfaces for Rockchip NPU platform to help users deploy RKNN models and accelerate the implementation of AI applications.
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RKNN Runtime provides C/C++ programming interfaces for Rockchip NPU platform to help users deploy RKNN models and accelerate the implementation of AI applications.
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RKNPU kernel driver is responsible for interacting with NPU hardware. It has been open source and can be found in the Rockchip kernel code.
- RK3566/RK3568 Series
- RK3588 Series
- RK3562 Series
- RV1103/RV1106
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
- 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
- RKNN-Toolkit2 is not compatible with RKNN-Toolkit
- Currently only support on:
- Ubuntu 18.04 python 3.6/3.7
- Ubuntu 20.04 python 3.8/3.9
- Ubuntu 22.04 python 3.10/3.11
- Latest version:1.6.0(Release version)
- Support ONNX model of OPSET 12~19
- Support custom operators (including CPU and GPU)
- Optimization operators support such as dynamic weighted convolution, Layernorm, RoiAlign, Softmax, ReduceL2, Gelu, GLU, etc.
- Added support for python3.7/3.9/3.11
- Add rknn_convert function
- Optimize transformer support
- Optimize the MatMul API, such as increasing the K limit length, RK3588 adding int4 * int4 -> int16 support, etc.
- Optimize RV1106 rknn_init initialization time, memory consumption, etc.
- RV1106 adds int16 support for some operators
- Fixed the problem that the convolution operator of RV1106 platform may make random errors in some cases.
- Optimize user manual
- Reconstruct the rknn model zoo and add support for multiple models such as detection, segmentation, OCR, and license plate recognition.
for older version, please refer CHANGELOG
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