ST AIoT Craft
这是一款在线工具，可利用带有传感器内AI的意法半导体组件快速开发传感器到云解决方案。
工具
创建、优化和部署您的机器学习算法。
- Create sensor-to-cloud solutions with ST boards
- Program the machine learning core to run in-sensor AI
- Explore end-to-end project examples.
ST AIoT Craft
1.0.0
这是一款在线工具，可利用带有传感器内AI的意法半导体组件快速开发传感器到云解决方案。
- Create sensor-to-cloud solutions with ST boards
- Program the machine learning core to run in-sensor AI
- Explore end-to-end project examples.
- Create sensor-to-cloud solutions with ST boards
Use the online tool to streamline the development of AI-enabled IoT nodes.
- Program the machine learning core to run in-sensor AI
Quickly create decision tree algorithms running on the machine learning core embedded in MEMS sensors.
- Explore end-to-end project examples.
Discover proof-of-concepts using ST reference designs.
NanoEdge AI Studio
一款免费的AutoML软件，用于向嵌入式项目添加边缘AI，并逐步引导用户轻松找到符合其需求的最佳AI模型。
- AutoML: automatic machine learning model generator
- Supports more than 1,000 Arm® Cortex®-M microcontrollers
- User-friendly software for end-to-end edge AI deployment
NanoEdge AI Studio
4.6
一款免费的AutoML软件，用于向嵌入式项目添加边缘AI，并逐步引导用户轻松找到符合其需求的最佳AI模型。
- AutoML: automatic machine learning model generator
- Supports more than 1,000 Arm® Cortex®-M microcontrollers
- User-friendly software for end-to-end edge AI deployment
- On-device learning for optimized anomaly detection
- AutoML: automatic machine learning model generator
NanoEdge AI Studio selects the best ML algorithm for a given microcontroller (low code / no code solution).
- Supports more than 1,000 Arm® Cortex®-M microcontrollers
Supports all STM32 microcontrollers, more than 100 ST development boards, 20 Arduino boards, and over 1,000 Arm® Cortex®-M microcontrollers.
- User-friendly software for end-to-end edge AI deployment
No expertize required. Create tiny, state-of-the-art AI models for microcontrollers in record time.
- On-device learning for optimized anomaly detection
NanoEdgeAI Studio offers a unique and patented approach that enables on-device learning directly on the microcontroller.
意法半导体边缘AI开发者云
一个免费的在线平台，可以轻松对边缘AI模型进行优化和基准测试，适用于多种意法半导体器件。该平台依靠意法半导体边缘AI内核来执行AI模型的优化和验证。
- Online AI benchmark service on multiple ST devices
- Optimize and profile your AI model
- Run your AI models on the ST board farm
意法半导体边缘AI开发者云
一个免费的在线平台，可以轻松对边缘AI模型进行优化和基准测试，适用于多种意法半导体器件。该平台依靠意法半导体边缘AI内核来执行AI模型的优化和验证。
- Online AI benchmark service on multiple ST devices
- Optimize and profile your AI model
- Run your AI models on the ST board farm
- Online AI benchmark service on multiple ST devices
Evaluate AI model performance in the cloud based on ST devices.
- Optimize and profile your AI model
Execute ST Edge AI Core technology to optimize your AI model and get insights into the model execution and performance on ST devices.
- Run your AI models on the ST board farm
Access the ST board farm and enjoy real-time access to physical ST products remotely. Review the performance of your AI models for the selected devices.
MEMS Studio
一套完整的桌面软件解决方案，用于在智能传感器上启用边缘AI功能。用户可利用该解决方案分析数据、评估嵌入式库，并为整个MEMS传感器系列设计无代码式算法。
- Collect, label, and analyze sensor data
- Profile and optimize NN and ML models for the ISPU
- Configure the MEMS machine learning core
MEMS Studio
1.3.0
一套完整的桌面软件解决方案，用于在智能传感器上启用边缘AI功能。用户可利用该解决方案分析数据、评估嵌入式库，并为整个MEMS传感器系列设计无代码式算法。
- Collect, label, and analyze sensor data
- Profile and optimize NN and ML models for the ISPU
- Configure the MEMS machine learning core
- Select the decision tree features and filters
- Collect, label, and analyze sensor data
Line charts of MEMS sensor data.
- Profile and optimize NN and ML models for the ISPU
Load, analyze, and evaluate models from Keras, ONNX, and TFLite on the intelligent sensor processing unit (ISPU).
- Configure the MEMS machine learning core
Collect data, generate decision trees, and configure sensors with a machine learning core (MLC).
- Select the decision tree features and filters
Use the automatic filtering and feature selection for a simpler MLC configuration process.
意法半导体边缘AI内核
一款用于优化和编译边缘AI模型的命令行接口 (CLI) 工具，适用于包括微控制器、微处理器和MEMS传感器在内的多种意法半导体器件。
- Import AI models from the most popular ML frameworks
- Review the detailed analysis of the selected algorithms
- Validate and optimize the models for different devices
意法半导体边缘AI内核
2.0.0
一款用于优化和编译边缘AI模型的命令行接口 (CLI) 工具，适用于包括微控制器、微处理器和MEMS传感器在内的多种意法半导体器件。
- Import AI models from the most popular ML frameworks
- Review the detailed analysis of the selected algorithms
- Validate and optimize the models for different devices
- Generate executable code for the selected ST hardware
- Import AI models from the most popular ML frameworks
Example of the installer.
- Review the detailed analysis of the selected algorithms
- Validate and optimize the models for different devices
- Generate executable code for the selected ST hardware
意法半导体边缘AI Model Zoo
一系列经过优化的参考边缘AI模型，可在附带相关部署脚本的意法半导体器件上运行。Model Zoo是一种宝贵的资源，可用于为嵌入式应用增添边缘AI功能。
- Choose from a collection of AI models optimized for ST devices
- Retrain any model from datasets using the scripts
- Deploy AI models on the application using the scripts
意法半导体边缘AI Model Zoo
一系列经过优化的参考边缘AI模型，可在附带相关部署脚本的意法半导体器件上运行。Model Zoo是一种宝贵的资源，可用于为嵌入式应用增添边缘AI功能。
- Choose from a collection of AI models optimized for ST devices
- Retrain any model from datasets using the scripts
- Deploy AI models on the application using the scripts
- Bring your own model (BYOM) or bring your own data (BYOD)
- Choose from a collection of AI models optimized for ST devices
The neural network models provided in the model zoo are optimized for various applications on the ST target devices.
- Retrain any model from datasets using the scripts
- Deploy AI models on the application using the scripts
- Bring your own model (BYOM) or bring your own data (BYOD)
STM32Cube.AI
X-CUBE-AI是一个免费的STM32Cube扩展包，可帮助开发人员将预训练的边缘AI算法（例如神经网络和机器学习模型）自动转换为经过优化的STM32 C代码。
- Optimize NN and ML models for STM32 platforms
- Profile NN and ML models on STM32 platforms
- Evaluate NN and ML models on STM32 platforms
STM32Cube.AI
9.0.0
X-CUBE-AI是一个免费的STM32Cube扩展包，可帮助开发人员将预训练的边缘AI算法（例如神经网络和机器学习模型）自动转换为经过优化的STM32 C代码。
- Optimize NN and ML models for STM32 platforms
- Profile NN and ML models on STM32 platforms
- Evaluate NN and ML models on STM32 platforms
- Available as an STM32CubeMX expansion package
- Optimize NN and ML models for STM32 platforms
Import your own neural network models into STM32CubeMX, select optimization options, and generate the optimized C code corresponding to the input models.
- Profile NN and ML models on STM32 platforms
X-CUBE-AI analyzes the NN model and generates a profiling report that details the NN memory requirements and the inference time, both for the complete network and for each layer.
- Evaluate NN and ML models on STM32 platforms
- Available as an STM32CubeMX expansion package
高速数据记录
该工具支持通过图形用户界面或命令行接口管理USB数据流的采集与标注工作。
高速数据记录固件兼容STBLESensor移动应用程序，并支持microSD™数据存储。
- Capture and monitor high-rate data
- Acquire and manage data with the Python SDK
- Port your project across multiple MCU series
高速数据记录
1.0.0
该工具支持通过图形用户界面或命令行接口管理USB数据流的采集与标注工作。
高速数据记录固件兼容STBLESensor移动应用程序，并支持microSD™数据存储。
- Capture and monitor high-rate data
- Acquire and manage data with the Python SDK
- Port your project across multiple MCU series
- Explore modular firmware examples
- Capture and monitor high-rate data
Monitor data acquisition and track live data on the user interface.
- Acquire and manage data with the Python SDK
Customize widgets for accurate data source management and visualization.
- Port your project across multiple MCU series
- Explore modular firmware examples
StellarStudioAI
面向Stellar电气化 (E) 微控制器的StellarStudio AI插件可在汽车系统中简化神经网络的开发，并提供自动的模型转换、执行和验证。
- Convert your AI models
- Create and review neural network performance reports
- Convert pretrained neural networks automatically
StellarStudioAI
1.2.0
面向Stellar电气化 (E) 微控制器的StellarStudio AI插件可在汽车系统中简化神经网络的开发，并提供自动的模型转换、执行和验证。
- Convert your AI models
- Create and review neural network performance reports
- Convert pretrained neural networks automatically
- Support for Keras, TensorFlow lite, ONNX
- Convert your AI models
Explore the GUI interface with Keras model generation.
- Create and review neural network performance reports
Use the IDE panel with validation and performance output.
- Convert pretrained neural networks automatically
Generate pretrained neural networks and convert them into efficient Ansi C libraries, which can be easily compiled, installed, and executed on Stellar E MCUs.
- Support for Keras, TensorFlow lite, ONNX
Integration of AI plug-in with the Stellar E development environment.
面向OpenSTLinux的AI
X-LINUX-AI是一个STM32 MPU OpenSTLinux扩展包，用于在STM32MP1和STM32MP2微处理器上运行边缘AI模型。其中包含了Linux® AI框架以及应用示例。
- Pose estimation (Yolov8n)
- Semantic segmentation (DeepLabv3)
- Image classification (MobileNetv2)
面向OpenSTLinux的AI
5.1.0
X-LINUX-AI是一个STM32 MPU OpenSTLinux扩展包，用于在STM32MP1和STM32MP2微处理器上运行边缘AI模型。其中包含了Linux® AI框架以及应用示例。
- Pose estimation (Yolov8n)
- Semantic segmentation (DeepLabv3)
- Image classification (MobileNetv2)
- Object detection (SSD MobileNetv2)
- Pose estimation (Yolov8n)
A collection of code examples for the most common computer vision applications.
- Semantic segmentation (DeepLabv3)
- Image classification (MobileNetv2)
- Object detection (SSD MobileNetv2)
手势识别ToF AI
该手势识别解决方案无需使用摄像头，即可基于意法半导体多区飞行时间传感器对一系列手势进行检测。
- Several possibilities to create dataset
- Seven hand postures recognized in the proposed dataset
- Based on an 8 x 8 ranging distance and signal rate
手势识别ToF AI
该手势识别解决方案无需使用摄像头，即可基于意法半导体多区飞行时间传感器对一系列手势进行检测。
- Several possibilities to create dataset
- Seven hand postures recognized in the proposed dataset
- Based on an 8 x 8 ranging distance and signal rate
- Deployed on an STM32 microcontroller
- Several possibilities to create dataset
Start with your own dataset or select an existing dataset.
- Seven hand postures recognized in the proposed dataset
The training process on ST or on custom datasets shows the related accuracy/loss charts and confusion matrix.
- Based on an 8 x 8 ranging distance and signal rate
- Deployed on an STM32 microcontroller
