# nncase

**Repository Path**: zgg110/nncase

## Basic Information

- **Project Name**: nncase
- **Description**: Open deep learning compiler stack for Kendryte K210 AI accelerator
- **Primary Language**: Unknown
- **License**: Apache-2.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-05-21
- **Last Updated**: 2021-05-21

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

<div align="center">
<img src="docs/logo.png" width="400" alt="nncase" />
</div>

[![License](https://img.shields.io/badge/license-Apache%202-blue)](https://raw.githubusercontent.com/kendryte/nncase/master/LICENSE) 
[![Build Status](https://dev.azure.com/sunnycase/nncase/_apis/build/status/kendryte.nncase?branchName=master)](https://dev.azure.com/sunnycase/nncase/_build/latest?definitionId=1&branchName=master)

`nncase` is a neural network compiler for AI accelerators.

`nncase` 是一个为 AI 加速器设计的神经网络编译器。
技术交流 QQ 群：790699378

## Install from binaries
## 从二进制安装

Download prebuilt binaries from [Release](https://github.com/kendryte/nncase/releases).

下载预编译的二进制文件 [Release](https://github.com/kendryte/nncase/releases)。

## Build from source
## 从源码编译

[Build from source](./docs/build.md)

## Supported operators
## 支持的算子

- [TFLite ops](./docs/tflite_ops.md)
- [Caffe ops](./docs/caffe_ops.md)
- [ONNX ops](./docs/onnx_ops.md)

## Usage
## 使用方法

- [Usage(English)](./docs/USAGE_EN.md)
- [FAQ(English)](./docs/FAQ_EN.md)
- [使用说明(中文)](./docs/USAGE_ZH.md)
- [常见问题(中文)](./docs/FAQ_ZH.md)


- [Examples 例子](./examples)

## Resources
## 资源

- [K210_Yolo_framework](https://github.com/zhen8838/K210_Yolo_framework)
- [Shts! 's Blog (Japanese)](https://www.shtsno24.tokyo/2020/03/nncase-v020.html)

---

## Architecture
## 架构

<div align="center">
<img src="docs/arch.png" alt="nncase arch" />
</div>

## Features

- Supports multiple inputs and outputs and multi-branch structure
- Static memory allocation, no heap memory acquired
- Operators fusion and optimizations
- Support float and quantized uint8 inference
- Support post quantization from float model with calibration dataset
- Flat model with zero copy loading

## 功能

- 支持多输入输出网络，支持多分支结构
- 静态内存分配，不需要堆内存
- 算子合并和优化
- 支持 float 和量化 uint8 推理
- 支持训练后量化，使用浮点模型和量化校准集
- 平坦模型，支持零拷贝加载