--- comments: true description: Deploy Ultralytics YOLO models on Axelera AI's Metis hardware. Learn how to export, compile, and run high-performance edge inference with up to 856 TOPS. keywords: Axelera AI, Metis AIPU, Voyager SDK, Edge AI, YOLOv8, YOLO26, Model Export, Computer Vision, PCIe, M.2, Object Detection, quantization --- # Axelera AI Export and Deployment !!! tip "Experimental Release" This is an experimental integration demonstrating deployment on Axelera Metis hardware. Full integration anticipated by **February 2026** with model export without requiring Axelera hardware and standard pip installation. Ultralytics partners with [Axelera AI](https://www.axelera.ai/) to enable high-performance, energy-efficient inference on [Edge AI](https://www.ultralytics.com/glossary/edge-ai) devices. Export and deploy **Ultralytics YOLO models** directly to the **Metis® AIPU** using the **Voyager SDK**. ![Axelera AI edge deployment ecosystem for YOLO](https://github.com/user-attachments/assets/c97a0297-390d-47df-bb13-ff1aa499f34a) Axelera AI provides dedicated hardware acceleration for [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) at the edge, using a proprietary dataflow architecture and [in-memory computing](https://www.ultralytics.com/glossary/edge-computing) to deliver up to **856 TOPS** with low power consumption. ## Selecting the Right Hardware Axelera AI offers various form factors to suit different deployment constraints. The chart below helps identify the optimal hardware for your Ultralytics YOLO deployment. ```mermaid graph TD A[Start: Select Deployment Target] --> B{Device Type?} B -->|Edge Server / Workstation| C{Throughput Needs?} B -->|Embedded / Robotics| D{Space Constraints?} B -->|Standalone / R&D| E[Dev Kits & Systems] C -->|Max Density
30+ Streams| F[**Metis PCIe x4**
856 TOPS] C -->|Standard PC
Low Profile| G[**Metis PCIe x1**
214 TOPS] D -->|Drones & Handhelds| H[**Metis M.2**
2280 M-Key] D -->|High Performance Embedded| I[**Metis M.2 MAX**
Extended Thermal] E -->|ARM-based All-in-One| J[**Metis Compute Board**
RK3588 + AIPU] E -->|Prototyping| K[**Arduino Portenta x8**
Integration Kit] click F "https://store.axelera.ai/" click G "https://store.axelera.ai/" click H "https://store.axelera.ai/" click J "https://store.axelera.ai/" ``` ## Hardware Portfolio The Axelera hardware lineup is optimized to run [Ultralytics YOLO26](https://docs.ultralytics.com/models/yolo26/) and legacy versions with high FPS-per-watt efficiency. ### Accelerator Cards These cards enable AI acceleration in existing host devices, facilitating [brownfield deployments](https://www.ultralytics.com/glossary/edge-computing). | Product | Form Factor | Compute | Performance (INT8) | Target Application | | :---------------- | :------------- | :----------------- | :----------------- | :----------------------------------------------------------------------------------------------------------------------------------------- | | **Metis PCIe x4** | PCIe Gen3 x16 | **4x** Metis AIPUs | **856 TOPS** | High-density [video analytics](https://docs.ultralytics.com/guides/analytics/), smart cities | | **Metis PCIe x1** | PCIe Gen3 x1 | **1x** Metis AIPU | **214 TOPS** | Industrial PCs, retail [queue management](https://docs.ultralytics.com/guides/queue-management/) | | **Metis M.2** | M.2 2280 M-Key | **1x** Metis AIPU | **214 TOPS** | [Drones](https://www.ultralytics.com/blog/build-ai-powered-drone-applications-with-ultralytics-yolo11), robotics, portable medical devices | | **Metis M.2 MAX** | M.2 2280 | **1x** Metis AIPU | **214 TOPS** | Environments requiring advanced thermal management | ### Integrated Systems For turnkey solutions, Axelera partners with manufacturers to provide systems pre-validated for the Metis AIPU. - **Metis Compute Board**: A standalone edge device pairing the Metis AIPU with a Rockchip RK3588 ARM CPU. - **Workstations**: Enterprise towers from **Dell** (Precision 3460XE) and **Lenovo** (ThinkStation P360 Ultra). - **Industrial PCs**: Ruggedized systems from **Advantech** and **Aetina** designed for [manufacturing automation](https://www.ultralytics.com/solutions/ai-in-manufacturing). ## Supported Tasks Currently, Object Detection models can be exported to the Axelera format. Additional tasks are being integrated: | Task | Status | | :----------------------------------------------------------------- | :----------- | | [Object Detection](https://docs.ultralytics.com/tasks/detect/) | ✅ Supported | | [Pose Estimation](https://docs.ultralytics.com/tasks/pose/) | Coming soon | | [Segmentation](https://docs.ultralytics.com/tasks/segment/) | Coming soon | | [Oriented Bounding Boxes](https://docs.ultralytics.com/tasks/obb/) | Coming soon | ## Installation !!! warning "Platform Requirements" Exporting to Axelera format requires: - **Operating System**: Linux only (Ubuntu 22.04/24.04 recommended) - **Hardware**: Axelera AI accelerator ([Metis devices](https://store.axelera.ai/)) - **Python**: Version 3.10 (3.11 and 3.12 coming soon) ### Ultralytics Installation ```bash pip install ultralytics ``` For detailed instructions, see our [Ultralytics Installation guide](../quickstart.md). If you encounter difficulties, consult our [Common Issues guide](../guides/yolo-common-issues.md). ### Axelera Driver Installation 1. Add the Axelera repository key: ```bash sudo sh -c "curl -fsSL https://software.axelera.ai/artifactory/api/security/keypair/axelera/public | gpg --dearmor -o /etc/apt/keyrings/axelera.gpg" ``` 2. Add the repository to apt: ```bash sudo sh -c "echo 'deb [signed-by=/etc/apt/keyrings/axelera.gpg] https://software.axelera.ai/artifactory/axelera-apt-source/ ubuntu22 main' > /etc/apt/sources.list.d/axelera.list" ``` 3. Install the SDK and load the driver: ```bash sudo apt update sudo apt install -y axelera-voyager-sdk-base sudo modprobe metis yes | sudo /opt/axelera/sdk/latest/axelera_fix_groups.sh $USER ``` ## Exporting YOLO Models to Axelera Export your trained YOLO models using the standard Ultralytics export command. !!! example "Export to Axelera Format" === "Python" ```python from ultralytics import YOLO # Load a YOLO26 model model = YOLO("yolo26n.pt") # Export to Axelera format model.export(format="axelera") # creates 'yolo26n_axelera_model' directory ``` === "CLI" ```bash yolo export model=yolo26n.pt format=axelera ``` ### Export Arguments | Argument | Type | Default | Description | | :--------- | :--------------- | :--------------- | :------------------------------------------------------------------------------------------- | | `format` | `str` | `'axelera'` | Target format for Axelera Metis AIPU hardware | | `imgsz` | `int` or `tuple` | `640` | Image size for model input | | `int8` | `bool` | `True` | Enable [INT8 quantization](https://www.ultralytics.com/glossary/model-quantization) for AIPU | | `data` | `str` | `'coco128.yaml'` | [Dataset](https://docs.ultralytics.com/datasets/) config for quantization calibration | | `fraction` | `float` | `1.0` | Fraction of dataset for calibration (100-400 images recommended) | | `device` | `str` | `None` | Export device: GPU (`device=0`) or CPU (`device=cpu`) | For all export options, see the [Export Mode documentation](https://docs.ultralytics.com/modes/export/). ### Output Structure ```text yolo26n_axelera_model/ ├── yolo26n.axm # Axelera model file └── metadata.yaml # Model metadata (classes, image size, etc.) ``` ## Running Inference Load the exported model with the Ultralytics API and run inference, similar to loading [ONNX](https://docs.ultralytics.com/integrations/onnx/) models. !!! example "Inference with Axelera Model" === "Python" ```python from ultralytics import YOLO # Load the exported Axelera model model = YOLO("yolo26n_axelera_model") # Run inference results = model("https://ultralytics.com/images/bus.jpg") # Process results for r in results: print(f"Detected {len(r.boxes)} objects") r.show() # Display results ``` === "CLI" ```bash yolo predict model='yolo26n_axelera_model' source='https://ultralytics.com/images/bus.jpg' ``` !!! warning "Known Issue" The first inference run may throw an `ImportError`. Subsequent runs will work correctly. This will be addressed in a future release. ## Inference Performance The Metis AIPU maximizes throughput while minimizing energy consumption. | Metric | Metis PCIe x4 | Metis M.2 | Note | | :------------------ | :------------ | :----------- | :---------------------- | | **Peak Throughput** | **856 TOPS** | 214 TOPS | INT8 Precision | | **YOLOv5m FPS** | **~1539 FPS** | ~326 FPS | 640x640 Input | | **YOLOv5s FPS** | N/A | **~827 FPS** | 640x640 Input | | **Efficiency** | High | Very High | Ideal for battery power | _Benchmarks based on Axelera AI data. Actual FPS depends on model size, batching, and input resolution._ ## Real-World Applications Ultralytics YOLO on Axelera hardware enables advanced edge computing solutions: - **Smart Retail**: Real-time [object counting](https://docs.ultralytics.com/guides/object-counting/) and [heatmap analytics](https://docs.ultralytics.com/guides/heatmaps/) for store optimization. - **Industrial Safety**: Low-latency [PPE detection](https://docs.ultralytics.com/datasets/detect/construction-ppe/) in manufacturing environments. - **Drone Analytics**: High-speed [object detection](https://docs.ultralytics.com/tasks/detect/) on UAVs for [agriculture](https://www.ultralytics.com/solutions/ai-in-agriculture) and search-and-rescue. - **Traffic Systems**: Edge-based [license plate recognition](https://www.ultralytics.com/blog/using-ultralytics-yolo11-for-automatic-number-plate-recognition) and [speed estimation](https://docs.ultralytics.com/guides/speed-estimation/). ## Recommended Workflow 1. **Train** your model using Ultralytics [Train Mode](https://docs.ultralytics.com/modes/train/) 2. **Export** to Axelera format using `model.export(format="axelera")` 3. **Validate** accuracy with `yolo val` to verify minimal quantization loss 4. **Predict** using `yolo predict` for qualitative validation ## Device Health Check Verify your Axelera device is functioning properly: ```bash . /opt/axelera/sdk/latest/axelera_activate.sh axdevice ``` For detailed diagnostics, see the [AxDevice documentation](https://github.com/axelera-ai-hub/voyager-sdk/blob/release/v1.5/docs/reference/axdevice.md). ## Maximum Performance This integration uses single-core configuration for compatibility. For production requiring maximum throughput, the [Axelera Voyager SDK](https://github.com/axelera-ai-hub/voyager-sdk) offers: - Multi-core utilization (quad-core Metis AIPU) - Streaming inference pipelines - Tiled inferencing for higher-resolution cameras See the [model-zoo](https://github.com/axelera-ai-hub/voyager-sdk/blob/release/v1.5/docs/reference/model_zoo.md) for FPS benchmarks or [contact Axelera](https://axelera.ai/contact-us) for production support. ## Known Issues !!! warning "Known Limitations" - **PyTorch 2.9 compatibility**: The first `yolo export format=axelera` command may fail due to automatic PyTorch downgrade to 2.8. Run the command a second time to succeed. - **M.2 power limitations**: Large or extra-large models may encounter runtime errors on M.2 accelerators due to power supply constraints. - **First inference ImportError**: The first inference run may throw an `ImportError`. Subsequent runs work correctly. For support, visit the [Axelera Community](https://community.axelera.ai/). ## FAQ ### What YOLO versions are supported on Axelera? The Voyager SDK supports export of [YOLOv8](https://docs.ultralytics.com/models/yolov8/) and [YOLO26](https://docs.ultralytics.com/models/yolo26/) models. ### Can I deploy custom-trained models? Yes. Any model trained using [Ultralytics Train Mode](https://docs.ultralytics.com/modes/train/) can be exported to the Axelera format, provided it uses supported layers and operations. ### How does INT8 quantization affect accuracy? Axelera's Voyager SDK automatically quantizes models for the mixed-precision AIPU architecture. For most [object detection](https://www.ultralytics.com/glossary/object-detection) tasks, the performance gains (higher FPS, lower power) significantly outweigh the minimal impact on [mAP](https://docs.ultralytics.com/guides/yolo-performance-metrics/). Quantization takes seconds to several hours depending on model size. Run `yolo val` after export to verify accuracy. ### How many calibration images should I use? We recommend 100 to 400 images. More than 400 provides no additional benefit and increases quantization time. Experiment with 100, 200, and 400 images to find the optimal balance. ### Where can I find the Voyager SDK? The SDK, drivers, and compiler tools are available via the [Axelera Developer Portal](https://www.axelera.ai/).