Unanswered questions

 I am considering installing a local AI system in my company.I kindly ask you to provide me with useful documentation to do this.Please consider a user who only has experience with C++ and Linux servers.Below is a brief project description.Project objectivesCreate an autonomous AI platform installed at your premises.Manage and monitor systems installed by customers (lights, sensors, cycles, errors, maintenance).Integrate Antholux3 and Topiaria2 into a single management architecture.Eliminate cloud dependencies → everything local: secure, scalable, proprietary.Create a technology hub for support, diagnostics, updates and remote assistance.General system architecture2.1 Main componentsLocal AI server (mini-PC, NUC or workstation)Internal databaseCommunication brokerRules engine + local AIUnified dashboardOTA module for updating remote systemsLogging and diagnostics system2.2 Technologies usedServer: Linux Debian/UbuntuDatabase: PostgreSQLBroker: MQTT (Mosquitto)UI: React or Angular + RES

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

Hello,I am very new to this so this question might be very basic. I am trying to run some very basic matrix multiplication application on the AIPU. I have a model forward doing only GEMM and exported to onnx. I tried doing as in the tutorial with a .yaml file to then deploy it and use it (maybe with axrunmodel?) but I am failing to do so as I don’t know what to put in the model info in the .yaml file. For example I don’t know what to put in the Task_category.Could you please explain how one would do that or tell me if I am doing all this completely wrong? Thank you very much

Hi,How can I implement the following preprocessing to run the end-to-end C/C++ pipeline?I've already implemented the build_gst function of the custom decoder with C++.def override_preprocess(self, img: PIL.Image.Image | np.ndarray) -> torch.Tensor:#trasformazione immagineimg = np.array(img)if img.ndim == 2:img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB)else:img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) img = np.float32(img/255.)#Stima rumoresigma_est = estimate_sigma(img, channel_axis=-1, average_sigmas=True)sigma_est = np.sqrt(sigma_est)sigma_est = min(sigma_est, 0.08) img = cv2.resize(img, (640,480), interpolation=cv2.INTER_LINEAR)img_tensor = torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float()noise_tensor = torch.full_like(img_tensor[:1, :, :], fill_value=sigma_est, dtype=torch.float32)final_tensor = torch.cat([img_tensor, noise_tensor], dim=0).to(torch.float32)return final_tensor

Buongiorno. sto valutando di inserire nella mia azienda un sistema locale di AI.Vi chiedo gentilemente di indicarmi documentazione utile ad fare questo.Per favore pensate ad una utenza che ha solo esperienza di C++ e Linux server.A seguire un breve documentazione di progetto.Obiettivi del progetto Creare una piattaforma AI autonoma installata presso la tua sede. Gestire e monitorare impianti installati dai clienti (luci, sensori, cicli, errori, manutenzione). Integrare Antholux3 e Topiaria2 in un’unica architettura di gestione. Eliminare dipendenze cloud → tutto locale: sicuro, scalabile, proprietario. Creare un polo tecnologico di supporto, diagnostica, aggiornamento e assistenza remota Architettura generale del sistema2.1 Componenti principali Server locale AI (mini-PC, NUC o workstation) Database interno Broker di comunicazione Motore regole + AI locale Dashboard unificata Modulo OTA per aggiornare gli impianti remoti Sistema di logging e diagnostica 2.2 Tecnolog

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

Project Title:NEMA SmartCare – Edge AI Assistant for Safety and Health in Homes, Schools, and WorkplacesShort Description:NEMA SmartCare is an edge-based AI system running on Orange Pi 5 Plus and the Metis M.2 accelerator. It monitors posture, falls, abnormal movements, environmental risks, and biometric data from external ESP32 sensors. The goal is to make homes, schools, gyms, hospitals, and offices safer by preventing accidents and supporting well-being.How It Works:Cameras analyze posture, movements, and the environment using AI models optimized for Metis.The AI detects falls, fainting, unsafe movements, prolonged bad posture, and risky behavior.Wearable sensors (ESP32 / MadBot) send real-time vital and motion data via Wi-Fi/Bluetooth.A local AI avatar (NEMA Edge) provides real-time feedback and guidance (e.g., “You are bending your back incorrectly”).In emergencies, it sends instant alerts to caregivers, relatives, or staff.Why It’s Innovative:Fully offline, privacy-preserving edg

I wanna use axelera metis on alinx board and I wonder that is there any yocto support? 

Hi,I’m trying to deploy a custom YOLO ONNX model on the Metis compute board using the Voyager SDK.The model fails during deployment because it expects (at least that’s what I understood^^) a fixed input shape of [8, 3, 128, 256], while the Voyager pipeline always provides a batch size of 1: Got: 1 Expected: 8My question is simple:👉 Is it possible to modify or extend the Voyager SDK (operators, pipeline, custom code, etc.) so that I can create a batch of 8 images before inference?Or is Voyager strictly unable to handle ONNX models with a fixed batch > 1, meaning the model must be re-exported with batch=1 or dynamic batch?Any guidance or examples would be appreciated.Thanks!

Hello,I’m currently trying to install the Voyager SDK (I tried v1.2.5 and the last version) inside a Docker container running Ubuntu 22.04, so I can later transfer the complete SDK onto my Metis Compute Board, which is currently empty (no sudo, no preinstalled SDK).However, no matter what I do, I always end up with a partial SDK installation — the folder size never exceeds ~80 MB, and key components such as ax_runner, ax_compiler, ax_engine, and axelera-runtime are missing.Here’s what I’ve already tried: Cloned the SDK from the official GitHub repo (https://github.com/axelera-ai-hub/voyager-sdk), as described in the installation guide. Successfully ran install.sh (after removing sudo calls since I’m root inside Docker). The installation completes without critical errors but stops with the message:  WARNING: Failed to refresh pcie and firmware The final folder size is about 80 MB, and python3 -m ax_runner --help fails with: No module named ax_runner I understand from the logs that

Hello again ! I’m currently exploring the Voyager SDK on my Metis Compute Board and everything works great with the pre-installed models.Now I’d like to integrate my own ONNX model into the SDK.I’ve seen that each model in Voyager uses a corresponding YAML configuration file that defines preprocessing, inference, and postprocessing parameters.Is there any official guide or documentation explaining: how to properly add a custom ONNX model to the SDK, what each section of the YAML file means (e.g., pipeline, models, datasets, operators), especially tricky ones like weight_md5 or else, and any important details or requirements when adapting an external model (like quantization, input format, etc.)? Thanks a lot in advance for your help!If there are any example templates or recommended practices, that would be really helpful.Best regards,Thibaut

Hey again !Finally I was able to install the voyager-SDK on my metis compute board ! Turns out I was debugging too much with chat gpt and it really does nonsense things I should have read more thoroughly the user guide for the metis or  the github repository… But now I have the SDK and I can test the different models directly in the SDK :)I successfully ran the yolov5s-v7-coco.yaml via ./inference <modelname> <media> But I can only run this model…I wonder if it’s normal and the other models in the folder ax_models/ are here but not compiled and need a certain action by the user (me), or if it is a bug and I’m doing something wrong ? When I try to run for example the yolov7 model with the argument --no-display just to see if it works : ./inference.py yolov7-coco media/traffic1_1080p.mp4 --no-display I have this error : 2025-11-05 15:46:12.084642319 [W:onnxruntime:Default, device_discovery.cc:164 DiscoverDevicesForPlatform] GPU device discovery failed: device_discovery.cc:89

Hi it’s still me haha,I’ve successfully installed the Voyager SDK v1.4.2 directly on my Metis Compute Board (Voyager Linux 1.0, Ubuntu 22.04 arm64 base).The full installation (install.sh --runtime --development) completed without any critical errors, and all Axelera packages (runtime, compiler, qtools, etc.) appear correctly in the Python environment.However, when I activate the SDK virtual environment and try to run: python3 -m ax_runner --helpi get : No module named ax_runnerHere’s a summary of my setup and verification steps:✅ Environment cat /etc/os-releaselsb_release -a✅ Installation cd ~/voyager-sdkbash install.sh --runtime --developmentsource venv/bin/activateThe install completed with: Installation complete. To activate the environment, type:  source venv/bin/activateWARNING: Failed to refresh pcie and firmware✅ Packages installed pip list | grep axelera→ returns: axelera-aipu-aingine-api     0.3.0rc1.post3.dev0+def3e9aaxelera-aipu-api             3.1.0rc3.post1.dev0+6327ad4axe

Hello,My team just received and opened a Metis Dev System with Arduino Portenta X8.We followed the “Getting Started with Metis Dev System with Arduino Portenta X8”,but could not go though; In installing the SDK;          “docker run -it --privileged -v /tmp:/tmp -v /axelera:/axelera -v /lib/modules:/lib/modules -v /run:/run -v /dev:/dev --group-add video -e DISPLAY=$DISPLAY --name=voyager-sdk-1.4.0 --network=host --entrypoint=/bin/bash axelera-sdk-ubuntu-2204-arm64-prebuid-llm:1.4.0”    failed with           “docker: Error response from daemon: pull access denied for axelera-sdk-ubuntu-2204-arm64-prebuild-llm, repository does not exist or may require ‘docketr login’: denied: requested access to the resource is denied” message          What is the default login ID/PASS of the docker image ?    2. Then, we tried the alternative ; after loading the downloaded docker image,          we moved into docker, and tried to finalize the setup.          But, in “make clobber-libs && make o

Radxa Orion O6 board does not boot with Metis PCIe card installed in its PCIe slot.Does not show even the BIOS screen.Tested some other cards (Intel 10GbE NICs) works all okay with the board.However, Metis PCIe card works nice with my PC and Orange Pi 5Plus board.Compatibility problem..?

Hi, The ONNX operator support information published on axelera-ai-hub appears to describe the status starting from opset 14 and newer. I would like to confirm the support status for opset 11. Could you please tell me? Best, regardsHiroyuki Kojima