Axelera Assist

Hi there,i´m working with an Axelera M.2 chip at uni. For some research on green AI i´m doing i want to track the power usage of the chip. As far as i know i need to track the power through the PCIe connection. Is there a way to track it with the AIPU directly or do i need to use the PCIe? And if there is a way can i save the usage in an array easily or is this a rather tricky situation?Thanks in advance Morruebe

Hello, I’m new to voyager SDK and everything related to AXELERA and i need help deploying a custom onnx model that has 2 inputs and 20+ outputs, all i want for now is to manage to run an inference with my own dataset/image and to save to disk the raw outputs of the model. The input is a pair of 2 files. I have data in yuv format and it’s saved in input_y.npy and input_uv.npy. Do you have any idea how can i do this or if it’s possible from the voyager 1.3 or do i need to add a split layer so i need just 1 input and only then i can deploy it?  Or to upgrade the voyager version to 1.4 

Will Metis M.2 ( AXE-BME20M1AR01B02) work with QNAP NAS?

Does anyone have any suggestions on how to implement SAHI inference with Yolo via voyager-sdk to run on metis m2?

Our development team requires specialized expertise for Axelera AI platform implementation and edge AI acceleration optimization. We're seeking an experienced consultant with 5-7weeks availability to support us with

Hello,I’m running inference with model  unet_fcn_512-cityscapes with pipe torch-aipu on the aetina eval board. It runs at 1.8 fps system and with 500ms of latency although device fps shows  11.5 fps capability. In the doc it is also mentioned that it should reach 18fps. I originally thought it was an issue with time wasted to load and decode png images from the SD card so I put them in shared memory but result are identical.I also tested yolov5s-v7-coco that should reach 805 fps but I can only achieve 214fps. Here are the output of: AXELERA_USE_CL_DOUBLE_BUFFER=0 ./inference.py yolov5s-v7-coco media/traffic3_720p.mp4 --show-stats --no-displayINFO : Deploying model yolov5s-v7-coco for 4 cores. This may take a while...|████████████████████████████████████████| 12:41.1 arm_release_ver: g13p0-01eac0, rk_so_ver: 9========================================================================

Hello everyone,I have been trying to compile the PRL Tracker model (see link below) for about a month using both Hailo and Vitis AI, but so far I have not been able to fully meet the requirements.I am now considering using the Metis Accelerator, but I am not sure whether I would run into the same issues.Has anyone here successfully deployed this model on the Metis Accelerator, or could you confirm if it is supported?Repository: https://github.com/vision4robotics/PRL-Track/tree/main Any guidance or shared experience would be greatly appreciated.Thanks in advance! 

Hello everyone, I am trying to use the Metis PCIe (4GB) in combination with the firefly ITX-3588J.Issue: I can not load the Metis kernel module (driver) into the Linux kernelSetup: I have the Metis PCIe with 4GB of ram  the Firefly ITX-3588J with 16GB of ram with a RockChip. OS: Ubuntu 22.04.4 LTS. I have a Gen3 Pcie with 4 lanes, details below```        LnkCap:    Port #0, Speed 8GT/s, Width x4, ASPM L0s L1, Exit Latency L0s <4us, L1 <16us            ClockPM- Surprise- LLActRep- BwNot- ASPMOptComp+``` So far:I installed the ubuntu OS as described here: https://support.axelera.ai/hc/en-us/articles/25556437653138-System-Imaging-Guide-Firefly-RK3588 I installed the latest 1.3.3 voyager sdk release.  i can see the metis board from lspci``` firefly@firefly:~$ lspci | grep accelerators01:00.0 Processing accelerators: Axelera AI Metis AIPU (rev 02)```however, i do not detect any drivers for the board, even after installing the sdk. Both the following commands do not return anything:```

Hi,Bit of a weird issue with the M.2 Evaluation System (SBC model AIB-MR1B-A1), and I’m worried it could be a hardware fault. I somehow got to a state where I would see AXR_ERROR_CONNECTION_ERROR: No target device found in lspci output".lspci didn’t show the accelerator card, but instead listed “Non-VGA unclassified device”:00:00.0 PCI bridge: Rockchip Electronics Co., Ltd RK3588 (rev 01)01:00.0 Non-VGA unclassified device: Synopsys, Inc. DWC_usb3 / PCIe bridgedmesg showed:Tue Sep 2 19:51:14 2025] rk-pcie fe170000.pcie: PCIe Linking... LTSSM is 0x3 [Tue Sep 2 19:51:16 2025] rk-pcie fe170000.pcie: PCIe Link Fail [Tue Sep 2 19:51:16 2025] rk-pcie fe170000.pcie: failed to initialize hostAfter trying a few things, I resorted to re-imaging the SBC. Then, on the first log-in from adb shell, I still saw Non-VGA unclassified device, but after a reboot, SSH’ing into the Eval System, I saw it as:00:00.0 PCI bridge: Rockchip Electronics Co., Ltd Device 3588 (rev 01)01:00.0 Processing accelerators

Hi, I'm working on an RPI5 and I have the voyager SDK inside a container with ubuntu 22 and it works. I wanted to try running an inference on a stream using GStreamer. First, I tried to access the stream and I managed with the following command:GST_PLUGIN_PATH=/voyager-sdk/operators/lib AXELERA_DEVICE_DIR=../opt/axelera/device-1.2.5-1/omega LD_LIBRARY_PATH=/voyager-sdk/operators/lib:/opt/axelera/runtime-1.2.5-1/lib:$LD_LIBRARY_PATH gst-launch-1.0 rtspsrc location='rtsp://…..' latency=200 ! rtph264depay ! avdec_h264 ! videoconvert ! autovideosink Now I wanted to try the axinferencenet plugin but I'm having problems. I've tested various commands and formats, such as: GST_PLUGIN_PATH=/voyager-sdk/operators/lib AXELERA_DEVICE_DIR=../opt/axelera/device-1.2.5-1/omega LD_LIBRARY_PATH=/voyager-sdk/operators/lib:/opt/axelera/runtime-1.2.5-1/lib:$LD_LIBRARY_PATH gst-launch-1.0 rtspsrc location='rtsp://...' latency=200 ! rtph264depay ! avdec_h264 ! videoconvert ! video/x-raw,format=BGRA !  axtran

Hi everyone,I'm currently developing a web application on top of the Axelera PCIe card and Voyager SDK, and I’ve hit a roadblock.Use Case Overview: I want to relay this annotated stream back to my web frontend for live viewing. I’ve tried: Using GStreamer pipelines for HLS (e.g., with hlssink) Embedding video using <video> tag on the frontend pointing to HLS or .mp4 System Details: Hardware: Axelera Metis PCIe Card Software: Voyager SDK (latest) YOLOv8n-based custom model Python 3.10 + GStreamer on Ubuntu 22.04

Hi,Recently I tried running some ROI tracking algorithms (the ones included with opencv, such as CSRT, MOSSE, etc) on an embedded ARM board and found that the tracking performance and latency is not as good as the application needs. One reason is that all these algorithms run on the CPU, which explains why they are not the fastest. Searching around, I found the VOT (Visual object tracking) challenge related to visual object tracking and it seems that most of the state-of-art trackers now use AI/ML. I guess that’s a good thing since in theory it should be easier to run these on dedicated AI accelerator hardware? Anyway, I want to try running a visual tracking algorithm on the Metis hardware to see how fast and well it can work. However, there are so many algorithms and I reckon the Metis may not support all the operators used in those networks? So before I embark on this task, i wanted to ask the Axelera experts for some guidance on what to go for and what to avoid in order to find a tr

Hi everyone,I’m new to working with the Axelera system. I see that there is a CenterCrop style preprocessing function, but in my use case I need more control. Specifically, I’d like to crop an image (for RTSP streams or training data) by specifying the x-offset, y-offset, width, and height of the region of interest.Is there a built-in preprocessing function in the Axelera SDK/YAML configs for this kind of custom crop? Or is the recommended way to handle this via GStreamer (e.g., videocrop/videobox) before passing frames to axinferencenet?Any help would be greatly appreciated.Thank you! 🙏

Thank you for your continued support.Could you please tell me the performance (fps) under the following conditions?1. METIS COMPUTE BOARD2. YOLOv8n model3. USB WEB CAMERA (1080P@60FPS)Thank you in advance.

Hello.I installed the SDK on Ubuntu 22.04, butit stopped with the following error:[172/194] Install libgstreamer-plugins-bad1.0-dev[172/194] E: Error, pkgProblemResolver::Resolve stopped. This is probably due to a non-modifiable package.ERROR: Failed to install libgstreamer-plugins-bad1.0-devI would appreciate any advice.

Hello,I’m deploying a model onnx (opset 17) and got a large bunch of warnings of unsupported operations. One of them is the unsupported Transpose:WARNING : Configuration of node '/backbone/3/0/Transpose' may not be supported. 'Transpose' parameters:WARNING : perm: [0, 3, 1, 2]WARNING : data: Metadata(shape=(1, 48, 64, 160), is_constant=False)WARNING : transposed: Metadata(shape=(1, 160, 48, 64), is_constant=False)WARNING : Unsatisfied constraint: perm == [0, 1, 2, 3]I guess the rule should have been perm != [0, 1, 2, 3] because here it is requesting a non no-op operation perm = [0, 3, 1, 2].I think this is also causing later the errorERROR : RuntimeError: The expanded size of the tensor (192) must match the existing size (160) at non-singleton dimension 1. Target sizes: [1, 192, 256, 160]. Tensor sizes: [1, 160, 1, 1]as it looks that indeed the transpose has not been applied.Could someone shade some light on this behavior?Thanks in advance 

Hello everyone,I'm new to the Axelera AI system and am currently working with a Metis PCIe card with 4 GB memory.I want to understand how much of this memory is being used by the neural network model I am deploying. In a way, I'm looking for a tool or command that provides a report similar to what nvidia-smi does for NVIDIA GPUs, but for the Metis core.From my research, I understand that the Metis core's DRAM is a fixed, dedicated resource and that the memory usage is determined at the time of compilation, not at runtime. I've heard that the Voyager SDK's compiler logs might contain this information.My main question is: Where in the compiler output or log files can I find the exact memory footprint and percentage utilization for my model? Is there a specific argument or command I need to use to generate this report?Any guidance on this would be greatly appreciated.Thank you!

Hello,I'm using the Metis M.2 accelerator with Aetina board, and I'm using Voyager SDK 1.4.0. I've been able to use the Metis M.2 card by issuing a reboot after resets, and that's good enough to work with for now. My code and model has been coming along, but I've got to the stage where I now wish to combine the models, to have a 2-stage pipeline.The aim is to have:(a) vehicle detection, followed by(b) license plate detection.  For (a), I'm using an existing detector (supplied with the SDK), which is yolov5m-v7-coco-tracker and I can successfully extract bounding box co-ords and images just as a test.For (b), I'm using a model I had to compile from ONNX format (using compile -i) The above is fine so far, but now I'm trying to chain them into a single vehicles-then-plates.yaml file, and that's unsuccessful. When I type ./deploy vehicles-then-plates, it silently exits, with no error. It may be a path issue, or it may be something else; I think I need help with the contents of that file.I'

The release notes for the v1.4 SDK state;“Compiled models are expected to run on Intel Core-i 12th and 13th generations (x86), AMD Ryzen (x86) and ARM64 host CPUs”.It would be good to support older generation Intel CPUs, so that servers from 5 or more years ago can be repurposed as AI edge accelerators.

Hello,I am having a hard time finding the source code of the metis-dkms.The one git repo I found as vcs.url does not exist publicly (https://github.com/axelera-ai/software-platform) I would love to have the sources because I need a rpm instead of deb-package. Cheers Tim

Hello everyone!I am Luca Gessi, from Italy. I am a (lucky) owner of Aetina board with M2 Axelera Metis. Unfortunatly the board arrived without the SDK installed. I installed it using the github guide. The procedure faced several issues and broken packages. I had to run it several time but at the end the installation completed.From this I tried to run simple command for testing the board (as suggested on the getting started guide):./inference.py --no-display yolov5s-v7-coco dataset However the inference failed: (venv) aetina@aetina:~/voyager-sdk$ ./inference.py --no-display yolov5s-v7-coco datasetERROR   : timeout for querying an inferencearm_release_ver: g13p0-01eac0, rk_so_ver: 9INFO    : Using dataset valINFO    : Dataset 'COCO2017' split 'val' downloaded successfully to /home/aetina/.cache/axelera/data/cocoINFO    : Dataset 'COCO2017' split 'labels' downloaded successfully to /home/aetina/.cache/axelera/data/cocoINFO    : Dataset 'COCO2017' split 'annotations' downloaded successfull

Hi,I tried the Yolo11-coco-onnx models from Model Zoo and got terrible results. Can anyone give me some advice? ./inference.py yolo11n-coco-onnx dataset --no-display --show-device-fpsINFO    : Model:      yolo11n-coco-onnx                                         INFO    : Dataset:    CocoDataset-COCO2017INFO    : Date:       2025-08-21 12:33:18.028455INFO    : Inference Time: 67178.89msINFO    : Evaluation Time: 36222.56msINFO    : Evaluation Metrics:INFO    : =========================INFO    : | mAP_box       | 0.03% |INFO    : | mAP50_box     | 0.05% |INFO    : | precision_box | 0.08% |INFO    : | recall_box    | 1.70% |INFO    : =========================INFO    : Key Metric (mAP_box): 0.03%INFO    : Core Temp : 49.0°C                                                    INFO    : Metis : 804.0fpsINFO    : CPU % : 31.4%INFO    : End-to-end : 74.7fps./inference.py yolo11s-coco-onnx dataset --no-display --show-device-fpsINFO    : Model:      yolo11s-coco-onnx                            

 SDK v1.3:14:32:45 [INFO] Dump used CLI arguments to: /repo/build/cli_args.json 14:32:45 [INFO] Dump used compiler configuration to: /repo/build/conf.json 14:32:45 [INFO] Input model has static input shape(s): ((1, 1, 2, 2),). Use it for quantization. 14:32:45 [INFO] Data layout of the input model: NCHW 14:32:45 [INFO] Using dataset of size 100 for calibration. 14:32:45 [INFO] In case of compilation failures, turn on 'save_error_artifact' and share the archive with Axelera AI. 14:32:45 [INFO] Quantizing '' using QToolsV2. 14:32:46 [INFO] ONNX model validation can be turned off by setting 'validate_operators' to 'False'. 14:32:46 [INFO] Checking ONNX model compatibility with the constraints of opset 17. Calibrating... ✨✨✨✨✨✨✨✨✨✨✨✨✨✨✨✨✨✨✨✨ | 100% | 390.54it/s | 100it | 14:32:46 [INFO] Exporting '' using GraphExporterV2. 14:32:47 [INFO] Quantization finished. 14:32:47 [INFO] Quantization took: 1.57 seconds. 14:32:47 [INFO] Export quantized model manifest to JSON file: /repo/build/quantize