AI Lounge

Check out our own ​@jonask-ai showing real-time, high-precision 8K object detection demo at Embedded World. An Aetina box, four Metis cards, and a whole lot of tile-based inference.What’s cool is it runs YOLOv8l across 380 overlapping tiles per second—no downscaling—at just 57% compute. And it’s not a one-off: same setup anyone can build with Voyager SDK + a YAML file.Noice!You can find out a bit more about this in ​@Doug’s blog post, too.

Hello,I am currently working on lane detection model (YOLOv2) which is in .pt format I have converted to ONNX format.Here are the terminal responses I have got..root@aetina:/voyager-sdk# cd YOLOPv2root@aetina:/voyager-sdk/YOLOPv2# lsLICENSE    data     demo2.py        models            runs   yolopv2-envREADME.md  demo.py  export-onnx.py  requirements.txt  utilsroot@aetina:/voyager-sdk/YOLOPv2# cd modelsroot@aetina:/voyager-sdk/YOLOPv2/models# ls__pycache__  common.py  experimental.py  yolopv2.onnx  yolopv2.ptroot@aetina:/voyager-sdk/YOLOPv2/models# I am having the ONNX format of the model so now I want to deploy the model onto Metis M2. When I am trying to compile the yolopv2.onnx model but i am encountering this issue.root@aetina:/voyager-sdk# /voyager-sdk/bin/voyager-compile models/yolopv2.onnx --output-dir build/yolopv2 --input-shape "1,3,640,640"bash: /voyager-sdk/bin/voyager-compile: No such file or directoryI request to guide me to successfully deploy the model.Note; I am using

This new survey suggests people (Apple fans, that is) are very open to the idea of paying a subscription for “Apple Intelligence” features. Which raises the question as to whether AI tools are going to become just another line on your monthly bill.We’ve moved quickly from “AI is cool” to “you might need it just to keep up.” And if it’s locked behind paywalls? That could become a problem once it’s a necessity rather than a nice-to-have.Curious what you all think – will AI be a must-have, or a paid perk for you?https://www.macrumors.com/2025/04/22/apple-intelligence-payment-survey/

This is a quick shoutout out to ​@ChinmayMulgund and ​@oguz for becoming the first members of Axelera AI’s community to earn the rank of Ensign! And practially at the same time (close enough that I can’t tell who got there first).Every activity on the community earns you points, and helps you climb the ranks. The opening rank is a Cadet, which you receive after your first action on the community. But it takes effort to climb the ranks, so a big congratulations to our first two Ensigns for getting there so quickly!Who’s going to be the first Lieutenant, I wonder? 

Gugging face just snapped up a humanoid robotics startup (Pollen Robotics, the folks behind Reachy 2). Open source robots are officially happening. Imagine a robot that serves coffee and critiques your model training while it’s doing it. I’d buy that!In all seriousness, it does actually feel like a big moment for open robotics.https://techcrunch.com/2025/04/14/hugging-face-buys-a-humanoid-robotics-startup/ 

hi there,i am currently writing up an application with friends for a grant on “ai&art” which is due by tomorrow.we want to do music reactive visuals on edge with multimodal inputs and want to use the Metis for live inference. i think of visuals being an interesting playground since they allow to very freely construct the loads for the chip. if you are interested in collaborating please feel free to reach out! there is no requirement on expertise.  ideas only are also very much welcome.(as well as additional sources for funding or sponsors)hope to have caught your interestbest carlo 

As part of the recent Arduino Day celebrations, our very own ​@Steven Hunsche joined with the Arduino team to demo the Metis PCIe running on a Portenta X8!This is an unmodified LLM, running in real-time, and completely offline.It doesn’t get much more edge than these two device working in combo 😄 And there’s an early access program in the works for anyone who’s interested in getting a closer look at Arduino’s and Axelera AI’s devices in action together: https://axelera.ai/development-systems/arduinoIn the meantime, check out the project on the Arduino Day live stream: 

There’s a lot of new hype everyday around new models that can do language + images + audio etc all at once. There’s a convenience to it, but is it the best way? Can’t help but think ”jack of all trades” when a new one is announced.I suppose it could be because of the chase towards the first AGI, but if you’re building an AI tool or platform, don’t you want a specialized model rather than a general one?Would be interested to hear if anyone here has actually built something on top of this — or tried and hit a wall.

Wanted to share a quick demo we’ve been playing around with — we’ve got the Metis M.2 up and running on a Raspberry Pi 5, doing real-time vehicle detection using an unmodified YOLOv8 model.Pretty smooth experience, getting them working together. Definitely still early days, so no promises yet on timelines or any kind of future support, but figured it was worth showing that it can be done!Curious — would people here be interested in standalone edge AI running on something like a Pi5? Any particular use cases that come to mind?

Just read a piece about Databricks working on this idea called TAO (Test-time Adaptive Optimization). Supposedly helps models improve themselves without clean labelled data — sort of like RL with synthetic practice rounds?Sounds clever, but also feels like one of those things that might work great in theory and fall apart in the wild. Anyone come across it in more detail or seen use cases outside research circles?