# rknn-toolkit2

**Repository Path**: calendar_day_1/rknn-toolkit2

## Basic Information

- **Project Name**: rknn-toolkit2
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2025-01-12
- **Last Updated**: 2025-01-12

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Description
  RKNN software stack can help users to quickly deploy AI models to Rockchip chips. The overall framework is as follows:
    <center class="half">
        <div style="background-color:#ffffff;">
        <img src="res/framework.png" title="RKNN"/>
    </center>

  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](https://console.zbox.filez.com/l/I00fc3), fetch code: rknn
- You can get more examples from [rknn mode zoo](https://github.com/airockchip/rknn_model_zoo)

# Notes
- RKNN-Toolkit2 is not compatible with [RKNN-Toolkit](https://github.com/airockchip/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](CHANGELOG.md)

# Feedback and Community Support
- [Redmine](https://redmine.rock-chips.com) (**Feedback recommended, Please consult our sales or FAE for the redmine account**)
- QQ Group Chat: 1025468710 (full, please join group 3)
- QQ Group Chat2: 547021958 (full, please join group 3)
- QQ Group Chat3: 469385426
<center class="half">
  <img width="200" height="200"  src="res/QQGroupQRCode.png" title="QQ Group Chat"/>
  <img width="200" height="200"  src="res/QQGroup2QRCode.png" title="QQ Group Chat2"/>
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