/rknn-toolkit2

Primary LanguageCBSD 3-Clause "New" or "Revised" LicenseBSD-3-Clause

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

  • RK3566/RK3568 Series
  • RK3588 Series
  • RK3576 Series
  • RK3562 Series
  • RV1103/RV1106
  • 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
  • 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:v2.0.0-beta0

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

v2.0.0-beta0

  • Support RK3576 (Beta)
  • Support RK2118 (Beta)
  • Support SDPA (Scaled Dot Product Attention) to improve transformer performance
  • Improve custom operators support
  • Improve MatMul API
  • Improve support for Reshape,Transpose,BatchLayernorm,Softmax,Deconv,Matmul,ScatterND etc.
  • Support pytorch 2.1
  • Improve support for QAT models of pytorch and onnx
  • Optimize automatic generation of C++ code

for older version, please refer CHANGELOG

Feedback and Community Support

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