Unanswered questions

I have a Metis Dev System board with Arduino x8. I followed the getting started guide  On the step to make the operators:make clobber-libs && make operatorsit fails. It gives error is as on the image below (sorry for photo instead of text)Any suggestions what is the root cause and how to fix it?

Hi,I have 3 axelera metis m2 device. I could not enable update for all of them. I have Voyager SDK 1.5.2 version that installed successfully. When i enter the command “./enable_bootloader_update.sh" it gives an error such as “Device communication timed out” and “Failed to load firmware”. I disabled the IOMMU option in BIOS and disabled the secure boot. My device is seen as “03:00.0 Processing accelerators: Axelera AI Metis AIPU (rev 02)" in the lspci output. Also when i enter the “axdevice” command it says : "[libtriton_linux.c:1313] Device communication timed out: device did not respond within 1 seconds. (0)WARNING: Failed to get valid board type for device metis-0:3:0 got 8Device 0: metis-0:3:0 board_type=unknown (not responding)"How can i solve this problem ? Can you help me ? The full terminal output of the command is that : (venv) ysf@ysf:~/Desktop/voyager-sdk$ ./enable_bootloader_update.shFirmware Flasher - Extracting files...Extracting firmware files...Files extracted successful

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 

Dear Team,I am currently trying to deploy a model that takes two input images as input. I get around that I have made changes to my model(OpticalFlow based Model) and it now takes (1,6,H,W). I have written a custom DataAdapter which makes sure that the input is concatenation of two images. I am getting following error when deploying ERROR :axelera.runtime: Expecting 2/1 input/output arguments, but got 1/1 instead.ERROR :axelera.app.pipe.torch: Pipeline error occurred: AXR_ERROR_INVALID_ARGUMENT: Expecting 2/1 input/output arguments, but got 1/1 instead.ERROR :axelera.app.pipe.torch: Traceback (most recent call last):ERROR :axelera.app.pipe.torch: File "/root/voyager-sdk/axelera/app/pipe/torch.py", line 52, in _loopERROR :axelera.app.pipe.torch: image, result, meta = model_pipe.inference.exec_torch(ERROR :axelera.app.pipe.torch: File "/root/voyager-sdk/axelera/app/operators/inference.py", line 1017, in exec_torchERROR :axelera.app.pipe.torch: self._axr_modeli.r

(venv) smart@smart-Default-string:~/voyager-sdk$ axdeviceERROR: No target device found in lspci outputERROR: AXR_ERROR_CONNECTION_ERROR: No target device found in lspci output(venv) smart@smart-Default-string:~/voyager-sdk$ axdevice --refresh[sudo] password for smart: ERROR: No target device found in lspci outputERROR: AXR_ERROR_CONNECTION_ERROR: No target device found in lspci output(venv) smart@smart-Default-string:~/voyager-sdk$ axdevice --refresh -vINFO: No Axelera devices found, will only rescan.INFO: PCIE rescanERROR: No target device found in lspci outputTraceback (most recent call last): File "/home/smart/.cache/axelera/venvs/644f17ff/bin/axdevice", line 8, in <module> sys.exit(entrypoint_main()) ^^^^^^^^^^^^^^^^^ File "/home/smart/.cache/axelera/venvs/644f17ff/lib/python3.12/site-packages/axelera/runtime/axdevice.py", line 794, in entrypoint_main main(args) File "/home/smart/.cache/axelera/venvs/644f17ff/lib/python3.12/site-packages/axelera/runtime/a

Will this ever be made available? I am interested in the 16gb version.   

Hey everyone,I'm trying to boot Ubuntu 22.04 from a SATA SSD on a Metis Compute Board (RK3588) running the Axelera BSP.Setup Board: Metis Compute Board (RK3588) BSP: Axelera Voyager Linux 1.2.1 (Mender-managed A/B boot) Goal: Boot Ubuntu 22.04 from SATA SSD (/dev/sda1) What I've Done Built a full Ubuntu 22.04 arm64 rootfs on the SATA SSD Copied BSP kernel modules (6.1.148-rockchip-standard) into the rootfs Set correct fstab using UUID Verified systemd and libc are present and valid Used mender_post_setup_commands to override mender_kernel_root to /dev/sda1 after Mender setup The ProblemMender overwrites mender_kernel_root every boot with the eMMC partition.Even after overriding it, the kernel attempts to boot from SATA but fails and then falls back to eMMC via mender_try_to_recover, leaving me stuck at a splash screen.I currently do not have UART access, so I can’t see kernel logs during the failed boot attempt.Additional NoteIf the system gets stuck at the splash screen pe

Hello, I would like to run Voxtral-Mini for Speech-To-Text (STT) and qwen3 tts 0.6B for Text-To-Speech. (TTS)Qwen3 tts would probably fit into a M.2 Metis AI board, but voxtral is a 4B model, therefore it would probably need a M.2 Metia AI Max.I have a few questions to start:I find a plenty of examples running Metis with images, but audio is almost not existent. Do you support it? If yes, can you point me to the specific documentation, please? If I use the 16GB of RAM of M.2 Metis AI Max, can I have two models loaded and running in parallel on the board? Voice applications are very sensitive to latency, being able to load two models and keeping them ready to work would keep latency low.Thank you, Ottavio

Hello Axelera team,I successfully got my Metis PCIe card (16 GB single-AIPU, Device 1f9d:1100) detected and running on a Dell PowerEdge R420 (Ubuntu 24.04 LTS, kernel 6.17.0-14-generic) using Voyager SDK release/v1.5.Quick summary how I got the hardware/driver working:Installed in a PCIe x4 slot (bus 0a:00.0) Disabled third-party PCIe fan response via IPMI raw command to stop jet-engine fans: ipmitool -I lanplus -H -U root -P calvin raw 0x30 0xce 0 0x16 5 0 0 0 5 0 1 0 0 Updated BIOS/iDRAC to latest available for R420 Cloned repo: `git clone https://github.com/axelera-ai-hub/voyager-sdk.git && cd voyager-sdk && git checkout release/v1.5` Ran `./install.sh --all --media` as non-root user Driver loaded successfully: `metis` module active, lspci shows "Axelera AI Metis AIPU (rev 02)", `/dev/metis-0:a:0` exists  `axdevice` confirms: "Device 0: metis-0:a:0 16GiB pcie flver=1.4.0 bcver=1.4 clock=800MHz(0-3:800MHz) mvm=0-3:100%"The install finishes with "Installation complete"

Hello,I am trying to understand how to add support for Metis to my embedded system, which is based on Buildroot.So far, this is what I have:I need to compile a kernel supporting DMA-BUF (done). I need to compile the kernel drivers (this is what I am working on now). I need to have the axelera_runtime package in my system, because the final application will depend on it.The last point is still unclear to me. I expected to find it in the Voyager SDK repository, but it is not there. The SDK depends on runtime files installed by the installation script, which downloads them from https://software.axelera.ai/ui/packages.Could you please clarify whether the source code for axelera_runtime is available? Or is it closed source?Thank you, Ottavio

Hi Team,I’m working with the Voyager SDK (using docker container) and trying to build the axstreamer and gstaxstreamer components. The source files examples reference:#include <axruntime/axruntime.hpp> (examples/axruntime/axruntime_example.cpp)But I can’t find axruntime.hpp anywhere in the SDK.I’m planning to run inference using a webcam as the input on my mac, with a pipeline that includes ffmpeg and mediamtx for streaming.Is axruntime supposed to be part of the SDK repo, a separate dependency, or is there a script I missed?Thanks!Val.

Hi,I just finished setting up everything and now I’m trying to run an example pipeline using.\inference.py yolov7-640x480-coco cars_clip.mp4Building the model seems to work fine but when the inference starts, it either crashes after some images withDetecting... : 2%|█▋ | 37/1802 [00:01<00:39, 44.29frames/s][ERROR][axeCommandQueueExecuteCommandListsAsync]: Wait kernel failed with return code -1.terminate called after throwing an instance of 'std::runtime_error' what(): axr_run_model failed with Error at zeCommandQueueExecuteCommandLists(cmdqueue, n_cmdlists, cmdlists, nullptr): cmdqueue_run_cmdlists: 319: Exit with error code: 0x7FFFFFFE : ZE_RESULT_ERROR_UNKNOWNAborted (core dumped)or instantly withINFO : Could not exec vainfo: Command '['vainfo']' returned non-zero exit status 3.WARNING : Timeout for querying an inferenceERROR : InferenceStream terminated due to T

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.

Hi,I’m interested to know if there is any way to measure AIPU power usage on Metis-equipped devices. The idea would be to measure yolo inference power usage on AIPU. 

I try to use Metis M.2 on Jetson Nano, I followed all the steps to install the board but when I try to run the first example from Voyager-SDK I get the following error:(venv) adrianza@ubuntu:~/voyager-sdk$ ./inference.py yolov5s-v7-coco media/traffic1_1080p.mp42026-02-05 09:54:10.578538286 [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"WARNING : Failed to get OpenCL platforms : clGetPlatformIDs failed: PLATFORM_NOT_FOUND_KHRWARNING : Please check the documentation for installation instructionsINFO : Deploying model yolov5s-v7-coco for 4 cores. This may take a while...|████████████████████████████████████████| 7:53.1 Opening in BLOCKING MODE NvMMLiteOpen : Block : BlockType = 261 NvMMLiteBlockCreate : Block : BlockType = 261 WARNING : capsfilter0: BUS: not negotiatedWARNING : (../libs/gst/base/gstbasetransform.c(1431): gst_base_tr

Completed the initial architecture implementation. The device can now track standard therapy exercises and show the count. Next step is to detect the pattern from an unknown exercise and then use it to help practice. I was also thinking of adding audio support - any recommendations on TTS models that can run with low latency?

Taming the GStreamer Beast: From 3 FPS to Real-Time1️⃣ The BottleneckWe hit a wall.Our Python-based torch-aipu pipeline was smart, but slow. It was handling video decoding, preprocessing, and visualization all on the host CPU.The result? 3.4 FPS on the host. Frames were piling up. Latency was unacceptable.To get real speed, we had to go lower level. We had to move the entire pipeline to GStreamer. 2️⃣ Building Custom OperatorsStandard GStreamer elements weren't enough. Optical flow is unique. It needs to see "time."We built a custom C++ Preprocess Operator. It acts as a short-term memory. It caches the previous frame. It stitches the "past" and "present" into a 6-channel tensor (NHWC) for the NPU.We also built a Postprocess Operator. The model outputs raw numbers. This operator converts that math into a colorful RGB flow map and pushes it downstream as a FlowImage. 3️⃣ Fighting the Crashes (The Real Work)Migration was not smooth. We fought issues across the entire stack. Here is the ba

Hi Axelera inferences and compiler available https://hub.degirum.com

Hi again, long time to see ^^After 6 months I saw a post about ORIN support and I wanted to try my Metis M.2 again.First I wanted to try it on my Desktop computer, ORIN will come after. But unfortunately, I got some errors.Note: I was able to run Metis M.2 on my desktop before.Now I am using same operating system (Ubuntu 22.04) but with different kernel version I suppose.I can install the repo succesfully but when I try to run “axdevice” command it gives me an error like this:[libdmabuf.c:1712] Device communication timed out: device did not respond within 1 seconds. (0)WARNING:axelera.runtime:Failed to get valid board type for device metis-0:2:0 got 5Device 0: metis-0:2:0 board_type=unknown (not responding)What I try to solve problem but didnt help:Disabled secure boot from BIOS Disabled Intel_iommu from grub entry Disabled pcie_aspm from grub entry Installed latest version of voyager (1.5.2) Installed previous version of voyager which I was able to run on April 2025 (1.2.5)Can you hel

I am looking for a way to infer Qwen VL 32b instruct A3b on the edge using Axelera m 2 form factor hardware. But I would need a lot of Ram to make this happen I am talking at least 48Gb. Could anyone share some light on what are my options. I need to do video inferencing and scene understanding on request from the user. My idea was to grab a few frames whenever the request was initiated and then choose the best frame to do inference.