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Hello, I am having issues reproducing the Axelera Model Zoo benchmarks on a Axelera Metis M.2 module.My issues seem to be related to pre/post processing, which is why I started with a classification model.I was hoping to use “fakevideo” as input, to bypass the pre-processing, but that seems to be fixed at 30fps.Another unresolved issue I have is that opencl is not working on my AMD PC.For ResNet-50 v1.5, here is what I am getting … (venv) voyager-sdk$ ./inference.py resnet50-imagenet data/coco --disable-opencl --no-display --show-stats========================================================================           Element                                         Time(𝜇s)   Effective FPS========================================================================axinplace-addstreamid0                                16        61,066.3vaapipostproc0                                     1,927           518.9videoconvert0                                         17        56,486.4axinplace0     

Sharing a quick build update.This short highlights part of the robotic hand assembly, with a focus on precision and high-quality 3D printing.The system is powered by Metis 2, integrated with Orange Pi, enabling efficient and scalable embedded AI.We are continuing development using Axelera AI technology as part of our system architecture.Feedback from the community is always appreciated.#AxeleraAI #Metis2 #EdgeAI #Robotics #EmbeddedAI #3DPrinting #MedTech

Hello.I recently received the m.2 metis accelerators and did a test on the boards provided by Axelera and got results close to the published benchmarks. It was succesful.I then connected these accelerators to the Orin (via the m.2 slot) in order to use them also for the my Jetson AGX Orin environment. But in the voyager-sdk installation i got “WARNING: Failed to refresh pcie and firmware” error. The installation completes but the accelerator device does not show up on my system. In lspci -tv command I get the output attached to this post. Can’t see metis there.I didn't see any information about the compatibility of these accelerators with Jetson systems (my m2 slot is M-key). If they are compatible, can you help me to fix the installation?Thanks :)

Hello,I looked through the model zoo provided in the github repo for voyager-sdk and could not find support for all the yolo11 pose estimation models. Specifically for yolo11m-pose and yolo11x-pose. Is support for these two models something that is being considered in the (near) future? Is it possible to deploy these two models with the repo being as is?

Hello, I would like to use the Metis AIPU to perform micro benchmarks, so very simple operations on which i would have the maximum control. Therefore i was wondering if there is a way to read and write for example in the cores’ L1 memory “manually”? For the moment i couldn’t find any of these, the closest being the DmaBufAllocator but i would guess it is not directly related to the core itself? Thank you very much 

HI ,I have an RK3588 board running Debian, and I would like to use it with an Axelera M.2 AI card. However, I cannot find any related installation or porting documentation.Does anyone have experience with this?Thank you.

can’t get the pcie card to show up with lspci on this lenovo thinkcenter 720q, fan powers up but doesnt show up

Hello,I am trying to open ssh server in my Metis SBC board (with 1x AIPU, 4 GB of RAM, 16GB of host RAM and active cooling, Rev1), but right now I’m in the sh $ console with the “antelao” user, and whenever I need to run a sudo command, I’m asked to enter the sudo password, which I don’t know. Can you help me with this or point me to the right tutorial/guide?Thanks a lot

On an AMD CPU with ubuntu 24.04, I see these warnings. Inference does open, but it seems to render poorly.(venv) my@machine:~/voyager-sdk$ ./inference.py yolov8l-coco-onnx media/traffic1_1080p.mp4 media/traffic1_1080p.mp4 media/traffic1_1080p.mp4 media/traffic1_1080p.mp4INFO    : Could not exec vainfo: Command '['vainfo']' returned non-zero exit status 3.WARNING : Failed to get OpenCL platforms : clGetPlatformIDs failed: PLATFORM_NOT_FOUND_KHRWARNING : Please check the documentation for installation instructionsOpenCL doesn't find any platforms(venv) my@machine:~/voyager-sdk$ clinfoNumber of platforms                               0ICD loader properties  ICD loader Name                                 OpenCL ICD Loader  ICD loader Vendor                               OCL Icd free software  ICD loader Version                              2.3.2  ICD loader Profile                              OpenCL 3.0

I have a metis M.2 device running on a RPI M.2 hat using the 1.2.4 device driver and the 1.4.2 SDK. However, when I tried to run inference, I ran into a DMA transfer issue. I tried to fix this by updating the AIPU firmware but the DMA transfer issue prevented the firmware from being updated.Host: Raspberry Pi 5OS: Raspberry Pi OS (Debian-based)Kernel: 6.12.47+rpt-rpi-2712Driver: metis 1.2.4SDK: 1.4.2Flash firmware: 1.3.2Issue: Device communication timeout (1 second) during firmware operationsPCIe: x1 link (Pi 5 M.2 limitation)Error: DMABUF_METIS_XFER failed, UnsupReq PCIe errorsDo you have any advice on how to proceed?

Hello everyone,I am new to Axelera and working with a Metis device (4 GiB, PCIe, Voyager SDK). Device: metis-0:1:0 Memory: 4GiB Clock: 800MHz SDK: voyager-sdk  I am trying to deploy the InsightFace model for face recognition and would appreciate guidance on model conversion, supported formats, and deployment steps.Thank you in advance,Akash

Hello,I’m currently working with the Metis Compute Board and the Voyager SDK, and I had a question regarding development outside of the SDK.Is it possible to run a script like a custom inference or a similar script directly on the board without going through the Voyager SDK pipeline ?I’m mainly asking to better understand the level of low-level access available on the Metis (runtime, APIs, or tools outside the SDK).Thanks in advance for your help.Best regards,

Hello,Following the question I had on running a GEMM only model, I have settled for a model that has Id Conv → Flatten → Gemm . I was wondering if the Flatten operation is just rearranging some pointer values or if it is doing a memory operation (like copying the tensor or something like that)?Thank you very much,

Hi,I'm using NanoPC-T6. I followed the Https://support.axelera.ai/hc/en-us/articles/31859388491794-Bring-up-Voyager-SDK-in-NanoPC-T6 you sent me. However, when I proceed with the test, the test will not proceed with the following error.https://community.axelera.ai/voyager-sdk-2/axelera-runtime-tar-gz-256?postid=2749#post2749 Someone has had the same issue in this community and I need help if there was a fix. (venv) pi@pi-desktop:~/voyager-sdk$ AXELERA_CONFIGURE_BOARD=,20 ./inference.py yolov8l-coco-onnx ./media/traffic1_480p.mp4 --no-display2026-01-02 14:31:47.360506524 [W:onnxruntime:Default, device_discovery.cc:164 DiscoverDevicesForPlatform] GPU device discovery failed: device_discovery.cc:89 ReadFileContents Failed to open file: "/sys/class/drm/card1/device/vendor"Failed to create device manager: libaxruntime.so: axr_create_context: libaxruntime.so: cannot open shared object file: No such file or directoryUnsupported tracer: core_temp: valid tracers are: cpu_usage, end_to_end_fps,

我们这个公司有股票了吗，该如何投资

Similar to https://community.axelera.ai/voyager-sdk-2/run-simple-gemm-model-1127, I got IndexError when converting a Gemm-only model via “compile -i model.onnx -o model --overwrite”. However, I don’t have Flatten layer. Could you help on checking what’s going wrong? The model I use and logs are attached below, and here are my system information:System: Ubuntu 22.04 x86_64 SDK: version 1.5.2 cloned from https://github.com/axelera-ai-hub/voyager-sdk

Hello everyone on the Axelera team!I'm having fun with the new Metis PCIe card that arrived!Thanks!I'm trying out voyager-sdk version 1.5.1 that supports this card, but it seems to throw an OpenCL runtime error and the sample won’t running.It doesn’t seem to be working with GST.For now, using the ‘--disable-opencl’ argument made it work.Alternatively,‘hardware_caps=config.HardwareCaps(…    opencl=config.HardwareEnable.disabel ...)source code.This isn't a solution, right? 

I’ve already redeployed the model:./inference.py yolo11m-custom dataset --show-device-fps --no-display --pipe gstarm_release_ver: g13p0-01eac0, rk_so_ver: 9INFO    : Using dataset valWARNING : Using deprecated heuristic transpose detection. Redeploy the model to include explicit output infoSegmentation fault

Hi,I installed Voyager SDK 1.5.1 on Ubuntu 22.04 and the Metis PCIe card is detected correctly, but running inference results in a segmentation fault.System Setup OS: Ubuntu 22.04 (AMD64) Hardware: Axelera Metis PCIe card SDK Version: Voyager SDK 1.5.1 GPU/CPU: AMD Ryzen + integrated GPU Installation completed successfully: No pending installation tasks. To activate the environment, type: source venv/bin/activate PCIe Card Detectionlspci -tv correctly shows the Metis device:+-01.1-[10]----00.0 Axelera AI Metis AIPU (rev 02) IssueWhen I run a sample inference:./inference.py yolov5s-v7-coco media/traffic1_1080p.mp4 --verbose I get:WARNING: Failed to get OpenCL platforms : clGetPlatformIDs failed: PLATFORM_NOT_FOUND_KHR Segmentation fault (core dumped) The last debug messages:DEBUG: Setting state of pipeline0 to GST_STATE_READY DEBUG: pipeline0: state-changed (null to ready) Segmentation fault (core dumped) What I’ve checked SDK installation is complete PCIe card detected Firm

Hi guys,After a small break, I was able to get the Metis M2 running on my Jetson Orin NX.However, is it possible that when using ‘axdownload’, the models are not precompiled? I tried to download ‘mobilenetv2-imagenet’, and got this error when trying to load it in. So, my guess is, I incorrectly assumed that the downloaded folder included a precompiled one? Or is there maybe a list of models which do include a precompiled model?Cheers (more info below),SebbeError-----------------------ERROR][zeModuleCreate]: Failed with error code: 0x70000004[ERROR][zeModuleCreate]: Build log:sh: 1: riscv64-unknown-elf-gcc: not foundCommand failed (32512)Failed to build module: sh: 1: riscv64-unknown-elf-gcc: not foundCommand failed (32512)[ERROR][zeKernelCreate]: Failed with error code: 2013265925Error at zeKernelCreate(module_handle, &desc, &kernel_handle): kernel_create: 409: Exit with error code: 0x78000005 : ZE_RESULT_ERROR_INVALID_NULL_HANDLEFailed to create handlesFailed to initialise AIC

Non c’è modo di aggiungere RAM a questa bellezza 😁?E’ perfetta per il mio Laptop o su Orange PI ( Embedded ).PCIe 5.0 ? LLM non gira su M.2 ? 😣Leggendo l’SDK… Interessante… 🤔

I just discovered the Smarter Spaces Challenge and I realize I am 48 hours past the deadline.However, I have a very strong project proposal ready ("EdgePark: Privacy-First Parking Monitor") and the specific technical skills to execute it immediately (experience with NVIDIA Jetson, Raspberry Pi, and custom YOLO models).Since I am confident I can deliver a high-quality project within the timeline, is there any possibility to still submit my application? I can send my proposal details immediately.Thank you for considering a passionate latecomer!Cheers, Tino

Hello,I am using an Axelera board (board type: AXR_BOARD_METIS_OMEGA_PCIE, firmware version: v1.4.2) and woul like to track the power consumption and current consumption of the AIPU while i am using it. I have tried using Axmonitor but it does not display power consumption (as announced in the SDK since i don’t have a 4-Metis AIPU card). Is there a way to monitor current consumption or power consumption (maybe both?), as there is for temperature?

Hello,I’m trying to test the AxInferenceNet C++ Integration.When I launch the test with the Voyager SDK’s given code and the yolov8s-coco model, the results of the detection are totally staggered. Display of the result :I followed the AxInferenceNet C++ Integration Tutorial and I’m using Voyager SDK with Ubuntu 22.04.Could someone help me to understand the results and give me a bunch of idea to solve it ? Thanks in advance.

Hello,I created a request from support.axelera.ai and I’m part of a team, how can i gave access to my request to my team?I saw that there is a tab in request “Request I’m CC’d on” so i think there is some implementation for this but i can’t find it anywhere.