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.
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
for older version, please refer CHANGELOG
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