78 lines
3.4 KiB
Markdown
78 lines
3.4 KiB
Markdown
# Description
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RKNN software stack can help users to quickly deploy AI models to Rockchip chips. The overall framework is as follows:
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<center class="half">
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<div style="background-color:#ffffff;">
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<img src="res/framework.png" title="RKNN"/>
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</center>
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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.
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# Support Platform
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- RK3566/RK3568 Series
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- RK3588 Series
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- RK3562 Series
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- RV1103/RV1106
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Note:
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**For RK1808/RV1109/RV1126/RK3399Pro, please refer to :**
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https://github.com/airockchip/rknn-toolkit
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https://github.com/airockchip/rknpu
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https://github.com/airockchip/RK3399Pro_npu
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# Download
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- You can also download all packages, docker image, examples, docs and platform-tools from [RKNPU2_SDK](https://console.zbox.filez.com/l/I00fc3), fetch code: rknn
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- You can get more examples from [rknn mode zoo](https://github.com/airockchip/rknn_model_zoo)
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# Notes
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- RKNN-Toolkit2 is not compatible with [RKNN-Toolkit](https://github.com/airockchip/rknn-toolkit)
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- Currently only support on:
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- Ubuntu 18.04 python 3.6/3.7
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- Ubuntu 20.04 python 3.8/3.9
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- Ubuntu 22.04 python 3.10/3.11
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- Latest version:1.6.0(Release version)
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# CHANGELOG
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## 1.6.0
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- Support ONNX model of OPSET 12~19
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- Support custom operators (including CPU and GPU)
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- Optimization operators support such as dynamic weighted convolution, Layernorm, RoiAlign, Softmax, ReduceL2, Gelu, GLU, etc.
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- Added support for python3.7/3.9/3.11
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- Add rknn_convert function
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- Optimize transformer support
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- Optimize the MatMul API, such as increasing the K limit length, RK3588 adding int4 * int4 -> int16 support, etc.
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- Optimize RV1106 rknn_init initialization time, memory consumption, etc.
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- RV1106 adds int16 support for some operators
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- Fixed the problem that the convolution operator of RV1106 platform may make random errors in some cases.
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- Optimize user manual
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- Reconstruct the rknn model zoo and add support for multiple models such as detection, segmentation, OCR, and license plate recognition.
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for older version, please refer [CHANGELOG](CHANGELOG.md)
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# Feedback and Community Support
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- [Redmine](https://redmine.rock-chips.com) (**Feedback recommended, Please consult our sales or FAE for the redmine account**)
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- QQ Group Chat: 1025468710 (full, please join group 3)
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- QQ Group Chat2: 547021958 (full, please join group 3)
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- QQ Group Chat3: 469385426
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<center class="half">
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<img width="200" height="200" src="res/QQGroupQRCode.png" title="QQ Group Chat"/>
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<img width="200" height="200" src="res/QQGroup2QRCode.png" title="QQ Group Chat2"/>
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<img width="200" height="200" src="res/QQGroup3QRCode.png" title="QQ Group Chat3"/>
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</center>
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