kendryte/nncase：为Kendryte AI加速器打开深度学习编译器堆栈

技术解读

以下内容由 GitRead 中文社区编辑团队基于项目 README 与官方资料整理，结合中文开发者的常见使用场景补充技术选型建议。具体以项目官方文档为准。

技术深度分析：Kendryte/nncase

项目背景与痛点

Kendryte/nncase 源自于凯迪思公司为自家 AI 架构所设计的一款深度学习编译栈。它本质上是为了解决微架构环境下深度学习模型部署的性能和效率问题。在边缘计算设备上运行深度学习模型通常面临计算资源有限与能效比低下的瓶颈。通过有针对性地编译器优化，nncase 帮助开发者更轻松地实现轻量级 AI 应用部署，适用于各类嵌入式系统。这意味着更好的能效比，同时降低了开发与部署的成本。

核心技术揭秘

nncase 的核心在于其编译优化技术栈的设计与实现。编译器的核心任务是将高级语言转化为机器语言，而在 nncase 中更是引入了一系列的中间表示与优化步骤来适配各种底层硬件特性。为了在低功耗硬件上高效运行，nncase 使用了许多精巧的设计。

graph TD A[模型定义] --> B("ONNX 优化") B --> C("代码生成与优化") C --> D("Kendryte 特定优化") D --> E[二进制输出] B -->|"Fusion & Quantization"| F{"模型优化决策"} F -->|"量化参数"| C

在上述流程中，模型从高级表示（如 ONNX）转换为优化更接近目标硬件的形式。重要的是它会执行量化及融合操作，通过减少计算精度来节省算力开销，同时保证模型性能不大幅降损。后面进行代码生成，进一步结合硬件特性如指令集架构特性优化指令序列以最佳利用计算资源。最终生成适合在 Kendryte 硬件上高质量运行的二进制文件。

功能亮点与差异

nncase 的独特之处在于对 Kendryte AI 芯片的深度优化，这归功于其特定的中间表示层与编译优化设计。这一工具包关注的是 AI 模型的能效比和可执行性，而不少竞品可能只偏重模型缩减。nncase 的优势在于代码生成与优化阶段，它能够针对特定的 AI 硬件架构产出高性能的执行代码，减少资源消耗，提升模型执行效率。

应用场景与落地建议

在生产环境中使用 nncase，开发者应该重点留意模型的大小与量化效果的平衡，因为这两者直接影响性能与准确性。选择合适的模型和优化策略对于成功部署至关重要。同时，在进行复杂模型的整理优化时，应充分考虑目标硬件的计算能力，确保模型不会超出硬件的性能边界。

这一编译栈非常适合需要在嵌入式设备、企业级或物联网等低能量消耗场景下运行深度学习任务的项目。综上所述，对于希望在嵌入式平台优化应用的人来说，nncase 至关重要，提供了执行深度学习任务的强大工具。

综合评价

Kendryte/nncase 有效解决了微架构下深度学习应用的性能优化问题。它凭借巧妙的编译优化技术，提升了其在嵌入式 AI 应用中的表现。然而，开发者在使用时需要了解其特定优化所影响的范围，尤其是硬件适配部分。总的来说，nncase 是一款强大的工具，适用于对低功耗高效率有严格要求的健康、安全监控、智能摄像头等智能物联网场景，可以大大提高计算效率和降低硬件需求。

关于

GitHub 原始描述（英文）

Open deep learning compiler stack for Kendryte AI accelerators ✨

neural-network deep-learning kendryte k210 k510 k230 ai-compiler llm compiler

Apache License 2.0
900 Stars
211 Forks
26 Watching

描述取自 GitHub 官方 API，保持仓库作者书写的原文。

简要分析

热度分

1348

价值分

605

活跃状态

活跃

GitHub

语言占比

C++

CMake

HLSL

HTML

Jinja

Python

Shell

Release

v2.11.02月28日
v2.10.02025年8月8日
nncase v2.9.02024年7月31日
nncase v2.8.32024年5月22日
nncase v2.8.22024年4月28日

贡献者

切换中文

nncase is a neural network compiler for AI accelerators.

Telegram: nncase community Technical Discussion QQ Group: 790699378 . Answer: 人工智能

K230

Install

Linux：
```
pip install nncase nncase-kpu
```

Windows:

1. pip install nncase
2. Download `nncase_kpu-2.x.x-py2.py3-none-win_amd64.whl` in below link.
3. pip install nncase_kpu-2.x.x-py2.py3-none-win_amd64.whl

All version of nncase and nncase-kpu in Release.

Supported operators

benchmark test

kind	model	shape	quant_type(If/W)	nncase_fps	tflite_onnx_result	accuracy	info
Image Classification	mobilenetv2	[1,224,224,3]	u8/u8	600.24	top-1 = 71.3% top-5 = 90.1%	top-1 = 71.1% top-5 = 90.0%	dataset(ImageNet 2012, 50000 images) tflite
	resnet50V2	[1,3,224,224]	u8/u8	86.17	top-1 = 75.44% top-5 = 92.56%	top-1 = 75.11% top-5 = 92.36%	dataset(ImageNet 2012, 50000 images) onnx
	yolov8s_cls	[1,3,224,224]	u8/u8	130.497	top-1 = 72.2% top-5 = 90.9%	top-1 = 72.2% top-5 = 90.8%	dataset(ImageNet 2012, 50000 images) yolov8s_cls(v8.0.207)
Object Detection	yolov5s_det	[1,3,640,640]	u8/u8	23.645	bbox mAP50-90 = 0.374 mAP50 = 0.567	bbox mAP50-90 = 0.369 mAP50 = 0.566	dataset(coco val2017, 5000 images) yolov5s_det(v7.0 tag, rect=False, conf=0.001, iou=0.65)
Object Detection	yolov8s_det	[1,3,640,640]	u8/u8	9.373	bbox mAP50-90 = 0.446 mAP50 = 0.612 mAP75 = 0.484	bbox mAP50-90 = 0.404 mAP50 = 0.593 mAP75 = 0.45	dataset(coco val2017, 5000 images) yolov8s_det(v8.0.207, rect = False)
Image Segmentation	yolov8s_seg	[1,3,640,640]	u8/u8	7.845	bbox mAP50-90 = 0.444 mAP50 = 0.606 mAP75 = 0.484 segm mAP50-90 = 0.371 mAP50 = 0.578 mAP75 = 0.396	bbox mAP50-90 = 0.444 mAP50 = 0.606 mAP75 = 0.484 segm mAP50-90 = 0.371 mAP50 = 0.579 mAP75 = 0.397	dataset(coco val2017, 5000 images) yolov8s_seg(v8.0.207, rect = False, conf_thres = 0.0008)
Pose Estimation	yolov8n_pose_320	[1,3,320,320]	u8/u8	36.066	bbox mAP50-90 = 0.6 mAP50 = 0.843 mAP75 = 0.654 keypoints mAP50-90 = 0.358 mAP50 = 0.646 mAP75 = 0.353	bbox mAP50-90 = 0.6 mAP50 = 0.841 mAP75 = 0.656 keypoints mAP50-90 = 0.359 mAP50 = 0.648 mAP75 = 0.357	dataset(coco val2017, 2346 images) yolov8n_pose(v8.0.207, rect = False)
	yolov8n_pose_640	[1,3,640,640]	u8/u8	10.88	bbox mAP50-90 = 0.694 mAP50 = 0.909 mAP75 = 0.776 keypoints mAP50-90 = 0.509 mAP50 = 0.798 mAP75 = 0.544	bbox mAP50-90 = 0.694 mAP50 = 0.909 mAP75 = 0.777 keypoints mAP50-90 = 0.508 mAP50 = 0.798 mAP75 = 0.54	dataset(coco val2017, 2346 images) yolov8n_pose(v8.0.207, rect = False)
	yolov8s_pose	[1,3,640,640]	u8/u8	5.568	bbox mAP50-90 = 0.733 mAP50 = 0.925 mAP75 = 0.818 keypoints mAP50-90 = 0.605 mAP50 = 0.857 mAP75 = 0.666	bbox mAP50-90 = 0.734 mAP50 = 0.925 mAP75 = 0.819 keypoints mAP50-90 = 0.604 mAP50 = 0.859 mAP75 = 0.669	dataset(coco val2017, 2346 images) yolov8s_pose(v8.0.207, rect = False)

Demo

eye gaze	space_resize	face pose

K210/K510

Supported operators

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

Architecture

Build from source

It is recommended to install nncase directly through pip. At present, the source code related to k510 and K230 chips is not open source, so it is not possible to use nncase-K510 and nncase-kpu (K230) directly by compiling source code.

If there are operators in your model that nncase does not yet support, you can request them in the issue or implement them yourself and submit the PR. Later versions will be integrated, or contact us to provide a temporary version. Here are the steps to compile nncase.

git clone https://github.com/kendryte/nncase.git
cd nncase
mkdir build && cd build

# Use Ninja
cmake .. -G Ninja -DCMAKE_BUILD_TYPE=Release -DCMAKE_INSTALL_PREFIX=./install
ninja && ninja install

# Use make
cmake .. -DCMAKE_BUILD_TYPE=Release -DCMAKE_INSTALL_PREFIX=./install
make && make install

Resources

Canaan developer community

Canaan developer community contains all resources related to K210, K510, and K230.

资料下载 --> Pre-compiled images available for the development boards corresponding to the three chips.
文档 --> Documents corresponding to the three chips.
模型库 --> Examples and code for industrial, security, educational and other scenarios that can be run on the K210 and K230.
模型训练 --> The model training platform for K210 and K230 supports the training of various scenarios.

Bilibili

Canaan AI tutorial and application demonstration

C: K230_SDK
- Documents
- K230 end-to-end tutorial
MicroPython: Canmv_k230
- Documents
- Examples

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nncase

技术解读

技术深度分析：Kendryte/nncase

项目背景与痛点

核心技术揭秘

功能亮点与差异

应用场景与落地建议

综合评价

相关仓库

关于

简要分析

语言占比

Release

贡献者

K230

Install

Supported operators

benchmark test

Demo

K210/K510

Supported operators

Features

Architecture

Build from source

Resources

Canaan developer community

Bilibili

K210 related repo

K230 related repo

评论