Unanswered questions

Hi, I have a custom U-Net model with MobileNetV2 encoder running on the Axelera portenta with voyager v1.4.0. I’ve based my code for inference on the axruntime python example in the Voyager github repository, with the model being compiled by the Voyager compiler. When running inference the maximum utilization of the Metis, as shown by axmonitor, only reaches 6% on all four cores. I have tried running it with all pre-processing and post-processing commented out and a dummy input consisting of all zeroes, but utilization also did not surpass 6%. I’m wondering whether this utilization limit might be caused by the way the model is compiled, perhaps due to needing multiple iterations in the LowerTIR stage, and whether there might be more optimal compiler configs to achieve a higher utilization. I have included some logging / debug files the CLI compiler generated for an older version of my model. For my current model I’m using the API compiler but it does not generate these files, the only

I recently acquired a Metis M.2 module and was so excited to get started with it. To my horror, I installed it into a standard, modern x86 mini PC (with power disconnected, all by the books), and the system was bricked upon my first attempt to boot. At first, I thought maybe the M.2 slot on the system was not providing adequate power for the device, and to the benefit of the Metis, I decided to try another host system. HUGE mistake. The same thing happened with a LattePanda x86 SBC, confirmed to have enough power to the M-key M.2 slot. These systems are not cheap, so to say I am disappointed is an incredible understatement.  Maybe I’m just not understanding where to look for host system specs, but it doesn’t seem like this issue is warned against anywhere in your docs or on the community page here. Has anyone else had this problem? I’m down two systems, so if someone could please tell me with certainty which system I CAN reliably plug this thing into, I would be very appreciative. EDIT

We currently run the Voyager SDK + Metis AIPU on a custom Yocto image (Scarthgap 5.0.18) on x86_64 (Ryzen 5 8500G / B650M). We're migrating the base OS to Ubuntu/Debian for hardware-agnostic driver support, and want to confirm a few things before starting:Is there an officially supported/tested Ubuntu or Debian version (and kernel version range) for the Metis driver + Voyager SDK on x86_64? Do you provide a .deb package or install script for the Metis kernel driver, or does it require a manual out-of-tree module build (e.g., via dkms)? Are there any known kernel version constraints or incompatibilities (e.g., minimum/maximum kernel version, specific config options required) we should account for when picking a distro kernel package? Any existing documentation or community reference for deploying the Metis driver outside of a Yocto-based image?

As the title suggests, I want to factory reset the board so that I can reinstall and setup the board from a clean state? How can I do it?

class FrameResult:image: Optional[types.Image] = Nonetensor: Optional[np.ndarray] = Nonemeta: Optional[AxMeta] = Nonestream_id: int = 0src_timestamp: int = 0sink_timestamp: int = 0render_timestamp: int = 0Could you explain when exactly these 3 timestamps are stamped? Our use case is we are trying to sync some telemetry samples with the exact moment the camera has recorded the frame. So it would be helpful to know what events trigger these timestamps.Thank you,

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

Hi,I'm trying to deploy a custom classification model on the Metis AIPU using the Voyager SDK. The model includes a CBAM attention block, but it fails during quantization with a topological sort error that points to a Gemm node.To verify that the issue wasn't in my model, I inspected the ONNX graph before quantization. The CBAM block contains only MatMul operations and no Gemm nodes. The only Gemm nodes in the model are in the final classifier head. Because of that, the Gemm mentioned in the error doesn't actually exist in the exported ONNX graph. It looks like the compiler is creating it internally during a MatMul to Gemm fusion pass, and somehow placing it before the node that produces one of its inputs.The graph pattern that seems to trigger this consists of two parallel MatMul branches that merge into a shared Add operation. As far as I know, this is a standard CBAM channel attention structure and not an unusual graph layout.Is this a known limitation of the compiler when handling

Hey everyone,I am evaluating the Metis lineup for an edge AI project and have a specific question about multi-card architecture that I haven't found a definitive answer to in the docs or SDK references.What I understand is supported today: The Voyager SDK detects multiple Metis cards in a single host. Each card runs its own independent inference pipeline. A real-world surveillance example shows three 4-chip PCIe cards running five primary models + one secondary model in parallel across 48 AIPU cores. My question: Is there any current or planned SDK support for splitting a single neural network across multiple Metis cards (e.g., layer sharding or tensor parallelism across two M.2 cards)? Or is the architecture strictly "one model must fit entirely within a single card's memory and AIPU"?Context for my use case: I am looking at a host with dual M.2 slots (e.g., Radxa Orion O6N) and weighing whether two standard Metis M.2 cards could act as a unified 2-GB / 428-TOPS accelerator for a

I received my Metis M.2 card yesterday and tried it out on Windows 11 on an ACEMAGIC F3A AMD Ryzen AI 9 HX 370 Mini PC using the instructions in https://docs.axelera.ai/sdk/user-guides/windows-setup/.  I had some troubles with the WSL part of the setup so I decided to skip that, and got to step 5 with the driver properly installed in device manager and the Axelera Windows packages installed. The axrunmodel sample returns the following output: [libaxldev.c:419] Firmware load address 0xffffffffffffffff exceeds 32-bit address space on Metis. WARNING:axelera.runtime:Failed to load runtime stage0: C:\AxeleraDevice-1.7.0\opt\axelera\device\omega\bin\start_axelera_runtime_stage0.bin [AxeleraDevice.cpp:455] Failed to get board DDR size. WARNING:axelera.runtime:Failed to get SW throttling from device metis-0:0:0 WARNING:axelera.runtime:Failed to get HW throttling from device metis-0:0:0 WARNING:axelera.runtime:Failed to get freq downscaling from device metis-0:0:0 [utils.c:264] sysctl_get_datas

Hi, when running inference on Metis M.2 on raspberry CM5 we noticed these errors occuring after couple minutes. error log: [libtriton_linux.c:531] DMABUF_METIS_XFER failed: Input/output error [AxeleraDmaBuf.cpp:222] DMA transfer failed: Input/output error [ERROR][axeShareMemoryExecute]: Dmabuf transfer failed. [ERROR][axeCommandQueueExecuteCommandListsAsync]: Level-zero memory operation failed: 0x70010001. 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: 0x70010001 : ZE_RESULT_ERROR_NOT_AVAILABLE kernel errror log: axl 0001:01:00.0: DMA error WR CH0 (ctrl 0x1) setup:  Runtime: runtime-1.5.3-1 - Voyager SDK: v1.5.2 - metis-dkms: 1.4.4 - Firmware: flash version 1.5.0 - bcver 7.1

Hello Axelera Community,I’ve been working on a project that I’m excited to share: a real-time face recognition home surveillance and attendance system using the Voyager SDK. My goal was to create a reliable system that could identify individuals from an RTSP stream and display their names accurately on-screen.I’m using a multi-model pipeline, starting with RetinaFace-Resnet50 for detection and then leveraging the FaceNet-LFW model for recognition, implemented through the face-recognition and face-recognition-with-vote components. While I've made progress, I’ve also encountered some challenges, particularly around database management and recognition accuracy. I wanted to share my approach and ask for advice from the community.My Current ApproachThe system architecture involves two main steps: Database Creation: I use the ./inference.py face-recognition faces/person/folder command to generate embeddings from images in my dataset. This creates a famous_embeddings.json file, which acts as

Subject Metis M.2 on Toradex Verdin i.MX95 enumerates over PCIe but all Axelera commands time out after (reportedly) successful firmware update; MSI interrupts never increment and board firmware version can no longer be queried Summary We are using an Axelera Metis M.2 accelerator on a Toradex Verdin i.MX95 WB on Mallow carrier. PCIe enumeration succeeds and the `metis` kernel driver binds to the endpoint, but all Axelera device commands fail to retrieve board/device information. The kernel repeatedly logs: axl 0000:01:00.0: IRQ MSI timeout (12 1) This persists with ARM SMMU passthrough enabled via `iommu.passthrough=1`. The most important observation is that the Metis MSI interrupt counters do not increment when an Axelera command is issued. The endpoint advertises MSI and the driver allocates MSI vectors, but the expected completion interrupt is never observed by Linux. Environment Host: Toradex Verdin iMX95 WB on Mallow BoardLinux 6.6.138-7.6.1-develTDX Wayland with XWayland 7.6.1-d

Hi,I have a C++ application using GStreamer on Windows that connects to a custom camera and performs preprocessing. I want to run a segmentation model on an Axelera Metis M.2 card.I installed the Windows packages and drivers successfully, but I don’t see any .lib files or clear way to link Axelera runtime directly into my C++ application on Windows.My goal is to integrate inference directly into my existing pipeline (ideally via GStreamer or better with a C++ API), rather than running a separate process.Questions:- Is native C++ integration on Windows supported (e.g., via .lib )? - What is the recommended architecture for integrating Axelera into an existing C++/GStreamer application on Windows?Thanks