Voyager SDK

Esistono e quali sono le architetture di riferimento ?Oppure dei benchmark ( in caso di tuning 🚗)...A scanso di equivoci e per un Proof-Of-Concept 😎

Why Voice Search SEO is the Future of Mobile Marketing1. The Mobile-First Era is Already HereThe mobile revolution has reshaped how we live, work, and interact with technology. Today, most users turn to their smartphones not just to browse, but to speak to search engines. Whether it’s asking Siri for the nearest coffee shop or commanding Google Assistant to play a song, voice interaction has become second nature.With over 70% of internet traffic now coming from mobile devices, optimizing for mobile-first behavior is no longer optional. Consumers are increasingly looking for faster, hands-free ways to access information—making voice search a major player in modern marketing.

I’ve used the yolov5s.pt file provided from the official yolov5 repository and converted it to onnx with this command:  python3 export.py --weights yolov5s.pt --imgsz 640 --batch-size 1 --include onnx --opset 17  . I’ve then tried to compile this with this command:  compile -i /home/axelera/Vision.AxeleraTesting/data/yolov5s.onnx -o /home/axelera/Vision.AxeleraTesting/data/compile --overwriteI got this output:09:49:45 [INFO] Dump used CLI arguments to: /home/vintecc/axelera/Vision.AxeleraTesting/data/compile/cli_args.json09:49:45 [INFO] Dump used compiler configuration to: /home/vintecc/axelera/Vision.AxeleraTesting/data/compile/conf.json09:49:45 [INFO] Input model has static input shape(s): ((1, 3, 640, 640),). Use it for quantization.09:49:45 [INFO] Data layout of the input model: NCHW09:49:45 [INFO] Using dataset of size 100 for calibration.09:49:45 [INFO] In case of compilation failures

Hi again ​@Spanner ! 😊I need to infer an image obtained from byte data stored in a memory space (https://docs.python.org/3/library/mmap.html) and after converting the bytes I get an image.I've seen the sources available for create_inference_stream and I know that I can make inferences to images (https://github.com/axelera-ai-hub/voyager-sdk/blob/release/v1.2.5/docs/tutorials/application.md), but I have to specify the/path/to/file. But in this case I won't have that information.In the code I'll have this line where I read the bytes in the memory space:        frame_size=image_width*image_height*3 //RGB        frame_bytes =

Hi, I’m getting very pool performance on Yolo11l custom weigts after the conversion in onnx.I’ve used opset 17 and 16, do you have any suggestions?Thanks.

Bonjour,Je n’arrive pas à récupérer le fichier `axelera-runtime.tar.gz`. Le script `install.sh` affiche “Runtime Installed OK” mais ne télécharge ni n’extrait quoi que ce soit.Aucune URL n’apparaît dans les logs et `libaxruntime.so` est absent de mon système.Merci de me fournir un lien de téléchargement direct pour installer le runtime manuellement.Cordialement,   

Dear all, I am trying to set up the metis PCIe accelerator on a intel workstation. Later we will move to a full risc-v embedded setup.In out lab workstation we are forced to use singularity instead of docker, so i am trying to translate the docker container into a singularity one.I think i am very close to make everything work. However, i am now stuck with a problem i can’t figure how to solve. in particular, i get a weird error when i try to run a simple ./inference.py with one of the pre-trained models. Below the full description on how i create, run the container, and how i run the example from the voyager sdk my `voyager-sdk-1.2.5.def` fileBootstrap: dockerFrom: ubuntu:22.04%labels Author You Version voyager-sdk-1.2.5%environment export DISPLAY=$DISPLAY%post apt-get update && apt-get install -y \

Hello, I try to use a YOLO model on 8 camera streams in parallel on the Metis PCIe card. 20-30 fps per camera stream is all I need. With the 548 fps end-to-end stated for YOLOv8s in https://axelera.ai/metis-aipu-benchmarks, it should be possible to reach ~68 fps per camera. As a small test I wrote a python script, which creates a inference stream with 8 videos as input:from axelera.app import config, display, inf_tracersfrom axelera.app.stream import create_inference_streamdef run(window, stream): for frame_result in stream: window.show(frame_result.image, frame_result.meta, frame_result.stream_id) fps = stream.get_all_metrics()['end_to_end_fp

Hi all,I’m a docker noob, so please bear with me if this is a stupid question.To get the SDK operational on my Ubuntu-24.04 system I followed the Install Voyager SDK in a Docker Container and can make it up to test-running some inferences. Alas, while I expected the videos to be displayed in a dedicated window at full resolution, for me they are shown in the active text-console (ncurses-based I assume), obviously coarse and slow.I tried to learn how to activate graphics output from within docker, but all I got was that docker essentially is meant to be text-only and graphics usually is provided via web-browser of pages served from within docker.So to put it bluntly: those videos showing Voyager SDK performing object detection at exhibitions at full-resolution and full-framerate, are thos

Hi there,I've been truly impressed by this chip over the past couple of days—its object detection and LLM capabilities, combined with its energy efficiency, are remarkable. However, for my current project, I need to use Gemma3 and Whisper. From what I understand, your SDK requires using precompiled paths, which suggests that if a model isn’t officially supported by Axelera AI, we might not be able to run it directly.Am I understanding this correctly, or is there an alternative way to use custom models like Gemma and Whisper within your ecosystem?Thanks in advance!

Hi, I followed the "Install Voyager SDK in a Docker Container" guide to try installing SDK version 1.2.5, but I encountered an error with one of the packages during the process.Could you please let me know how to resolve this issue?The attached file contains the installation log. ---    [150/193] Installing development libraries    [150/193] Install axelera_compiler    [150/193] python3 -m pip --disable-pip-version-check install --index-url XXXX@software.axelera.ai/artifactory/axelera-dev-pypi/ --no-deps -c cfg//requirements-ubuntu-2204-amd64.txt "axelera_compiler"    [150/193] Looking in indexes: XXXX:****@software.axelera.ai/artifactory/axelera-dev-pypi/    [150/193] WARNING: 401 Error, Credentials not correct for https://software.axelera.ai/artifactory/axelera-dev-pypi/axelera-compiler/(X)    [150/193] ERROR: Cannot install axelera_compiler because these package versions have conflicting dependencies.

it ask me every time about token … yes I use my registered emailand I did not found any token in my accountwtf

Seems Ubuntu 24.04 is not supported at the moment. What are the plans for this version? Can you give some insight on what needs to be changed to get this installed on that version?

I am encountering issues when trying to deploy and run the YOLOv5m6 ONNX model on the Metis M.2. Below are the details of the steps I followed and the issues that arose:1. Deployment Process:I used the following command to deploy the model:./deploy.py customers/mymodels/yolov5m6-object-detection.yamlThe deployment was successful, but I encountered warnings about memory overflow during the buffer fitting process:LowerTIR failed to fit buffers into memory after iteration 0/4. Pool usage: {L1: alloc:4,195,840B avail:4,194,304B over:1,536B util:100.04%, L2: alloc:6,864,384B avail:8,077,312B over:0B util:84.98%, DDR: alloc:54,275,328B avail:260,046,848B over:0B util:20.87%}2. Inference Process:I then attempted to run inference with the following command:./inference.py c-yolov5m6-object-detection chinmay/Vehicle-CV

Hi,I know Metis can be used for ML etc. but wondering whether it’s suitable to do some more low-level operations like big matrix multiplication, FFT of data stored in Metis memory etc. - more like on CUDA (some DSP/Math library)? I assume I could use torch basic math functions? Dominik 

hey,I am an ARM enthusiast and I wanted to check if you have any compatibility list.Are you supporting NXP and MediaTek?What about the Broadcom CPU on latest Raspberry Pi?

Is there a preferred way to suggest new features or request model support? Also wondering if there’s a public roadmap or at least a rough idea of what’s coming up in the next releases (especially around expanded model support or tools for deployment).Would be great to understand how other users here are submitting feedback — and whether the Axelera team actively tracks suggestions from this forum too. Cheers!

Hi!I need to set up a real-time security surveillance system to monitor an area using 20 cameras. Using your technology, what is the best solution you would propose, including the components and setup required to make it work efficiently? Additionally, could you let me know how I can purchase the recommended solution?Kind regards,G