Spaces:
Running
Running
File size: 6,765 Bytes
4a991ff 309c3d7 53a826b 309c3d7 4a991ff 1dba911 4a991ff 309c3d7 1dba911 309c3d7 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 | ---
title: README
emoji: ⚡
colorFrom: red
colorTo: gray
sdk: static
pinned: false
---
<style>
.st-header-banner {
width: 100%;
height: 120px;
margin: 0 0 12px 0;
padding: 0 24px;
box-sizing: border-box;
background: #03234B;
display: flex;
align-items: center;
justify-content: flex-start;
}
.st-logo-right {
height: 72px;
width: auto;
object-fit: contain;
}
img.rotating-content {
width: 300px;
height: 150px;
object-fit:cover;
object-position:center;
}
.icon {
width: 1.5em;
height: 1.5em;
vertical-align: -.7em;
padding: .25em .5em .25em .25em;
}
ul.social {
list-style: none;
padding-left: 0;
}
ul.social li {
padding-left: .5em;
display: flex;
}
.architectureImage {
border: 0.5px solid black;
}
</style>
<div class="st-header-banner">
<img src="assets/ST_logo_2024_white.png" alt="ST Logo" class="st-logo-right"/>
</div>
_**Innovating with edge AI on STM32 and Hugging Face.**_
STMicroelectronics is a global semiconductor leader pushing artificial intelligence down to the most resource-constrained microcontrollers. With the **STM32 AI ecosystem**, ST provides an end-to-end pipeline — from pre-trained models in the **Model Zoo** to bare-metal optimized deployment — enabling embedded developers to build intelligent applications without deep ML expertise.
Models are optimized, quantized and validated to run directly on ST Neural-ART but also Cortex-M4, M7, M85 and M33 cores.
---
## End-to-End AI Pipeline
```
+----------------------------+
| EXPLORE |
+----------------------------+
| STM32 AI Model Zoo |
+----------------------------+
|
v
+----------------------------+
| TRAIN |
+----------------------------+
| STM32 AI Model Zoo |
| Services |
+----------------------------+
|
v
+----------------------------+
| OPTIMIZE / QUANTIZE |
+----------------------------+
| STM32 AI Model Zoo |
| Services |
+----------------------------+
|
v
+----------------------------+
| EVALUATE / PREDICT |
+----------------------------+
| STM32 AI Model Zoo |
| Services |
+----------------------------+
|
v
+----------------------------+
| BENCHMARK |
+----------------------------+
| STM32Cube AI Studio |
| STM32 Developer Cloud |
+----------------------------+
|
v
+----------------------------+
| CONVERT |
+----------------------------+
| STM32Cube AI Studio |
| ST Edge AI Core |
+----------------------------+
|
v
+----------------------------+
| DEPLOY |
+----------------------------+
| STM32Cube ecosystem |
| (tools, middleware, BSP) |
+----------------------------+
```
This diagram summarizes the typical STM32 edge AI workflow from model discovery to on-device deployment:
1. **Explore**: Start from the STM32 AI Model Zoo to browse available architectures, pretrained checkpoints, and application examples.
2. **Train**: Use Model Zoo Services to retrain an existing model or build a task-specific pipeline on your own dataset.
3. **Optimize / Quantize**: Reduce model size and compute cost so the network fits embedded constraints while preserving the best possible accuracy.
4. **Evaluate / Predict**: Validate accuracy, inspect predictions, and compare tradeoffs before moving to hardware execution.
5. **Benchmark**: Measure latency, memory footprint, and target compatibility with STM32Cube AI Studio and STM32 Developer Cloud.
6. **Convert**: Transform the trained model into STM32-ready artifacts using STM32Cube AI Studio and ST Edge AI Core.
7. **Deploy**: Integrate the generated code into the STM32Cube ecosystem, including firmware, middleware, and board support components.
In short, the flow shows how a model moves from selection and training to optimization, hardware validation, and final integration on STM32 devices.
## Build, Optimize and Deploy AI/ML on STM32
- **STM32 AI Model Zoo**: A GitHub collection of reference machine learning models optimized for STM32 microcontrollers.
- **Application-Oriented Model Library**: A large set of models ready for re-training across multiple use cases.
- **Pre-trained Models Across Frameworks**: Reference models variants available for PyTorch, TensorFlow, and ONNX workflows.
- **End-to-End Scripts & Services**: Tools to retrain, quantize, evaluate, and benchmark models on custom datasets, plus autogenerated application code examples via [stm32ai-modelzoo-services](https://github.com/STMicroelectronics/stm32ai-modelzoo-services/tree/main)
- **Fast Deployment + Full Customization**: Use pretrained categories for quick deployment, or apply transfer learning / full training from scratch on your own data.
- **Reference Performance Metrics**: Results provided on STM32 MCU, NPU, and MPU targets for both float and quantized models.
- **Expanded Framework Support**: Comprehensive PyTorch support complements TensorFlow and ONNX in unified end-to-end workflows (train, evaluate, quantize, benchmark, deploy).
---
## Key Tools & Ecosystem
- **STEdgeAI Core**: Converts trained neural networks into optimized C code for STM32.
- **STM32 AI Model Zoo services**: This repository provide scripts and workflows to ease end-to-end AI model training and integration on ST devices. They offer a valuable foundation to add AI capabilities to STM32-based projects.
- **STM32 AI Model Zoo** The repository with a of reference pre-trained machine learning models optimized for STM32 microcontrollers generated thanks to the STM32 AI Model Zoo services.
- **Integration with Popular Frameworks**:
- TensorFlow / Keras
- PyTorch (via ONNX export)
- ONNX Runtime pipelines
---
## Links
- **[STM32 AI Model Zoo services](https://github.com/STMicroelectronics/stm32ai-modelzoo-services/tree/main)**
- **[STEdgeAI Core](https://www.st.com/en/development-tools/stedgeai-core.html)**
- **[STM32 Developer Cloud](https://stm32ai-cs.st.com/home)**
- **[STM32AI Model Zoo](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main)**
- **[STM32AI Cube Studio](https://www.st.com/en/development-tools/stedgeai-cubeai.html)**
---
## 🤝 Contact & Contributions
- For technical questions: [ST EdgeAI Community](https://community.st.com/t5/edge-ai/bd-p/edge-ai)
- For issues or feature requests, use the **Issues** or **Discussions** tabs in the respective repos.
- Contributions and feedback on models, pipelines, and docs are welcome. |