Qualcomm AI Hub

Real-time LiDAR object detection is computationally expensive. Running it locally on the edge without latency spikes is even harder. CenterPoint is now available and optimized on Qualcomm AI Hub Models. This 3D object detection model predicts object centers and regresses other attributes specifically for autonomous driving scenarios. It's engineered for high accuracy and real-time performance on the road. The Specs: • Checkpoint: PointPillars • Parameters: 21.8M • Footprint: 83.3 MB You can run this locally across a wide range of hardware, from Snapdragon 8 Elite Gen 5 QRD to the Dragonwing IQ-9075 EVK or Snapdragon 8 Elite Gen 5. No cloud dependency required. Grab the optimized model and test it on your target hardware today: https://lnkd.in/gDacH7_u #EdgeAI #ComputerVision #AutonomousDriving #QualcommAIHub #MachineLearning

For your next ML model development, test across a massive list of real Qualcomm powered devices at zero cost to you. Here’s how it works: When you run inference, you aren't just getting theoretical numbers, Qualcomm AI Hub Workbench actually runs your model on a real, physical Qualcomm device hosted in the cloud. Just pick which device(s) you're interested in! Want to see how your vision model handles on a Snapdragon 8 Gen 3? Or test the latency for your next compute app on a Snapdragon X2 Elite? You can see exactly how it runs on the actual chipset without needing to go buy it. Stop guessing and start building with real hardware data. Check out the full list of supported devices you can test on right now: https://lnkd.in/g9xNauW4 #Qualcomm #EdgeAI #MakerCommunity #MachineLearning #QualcommAIHub

Thank you to all the developers that joined us yesterday at our hands on deep dive session at Edge AI and Vision Alliance's Embedded Vision Summit 2026! We enjoyed having a packed room of 75 developers - discussing model iteration, accuracy tradeoffs, compute unit and runtime tradeoffs and how to test their models on real devices with AI Hub Workbench! Tag us if you were there and what you're planning to deploy on-device with AI Hub!

Just a quick reminder for the builders out there: All Qualcomm AI Hub Models and their details are directly on GitHub. You do not have to piece together fragmented documentation to get models like Llama, Whisper, or YOLO running on Qualcomm devices. The team has pre exported and tuned the Python scripts so you can clone the repo and get your hardware running immediately. Everything is fully open, optimized, and ready to run. Grab the code and start building your next project right here: https://lnkd.in/gfkpFGzK #EdgeAI #QualcommAIHub #MachineLearning #GitHub #DeveloperCommunity

Imagine building a smart camera or an autonomous rover that can map complex scenes locally, without draining the battery or sending a single byte to the cloud. That is exactly what InternImage brings to your edge projects. The architecture was designed to solve a major roadblock in local visual recognition, and the core idea is brilliant: Instead of relying on the rigid kernels of traditional CNNs or the heavy memory footprint of Vision Transformers, it uses DCNv3 (Deformable Convolutions version 3). This custom operator lets the network dynamically adjust its focus to the most important parts of the image on the fly. The results speak for themselves. It achieved a 90.1% Top 1 accuracy on ImageNet and a massive 65.5 mAP on the COCO dataset. You get transformer level accuracy with a computational footprint that actually makes sense for local hardware. InternImage is now available on Qualcomm AI Hub. It is optimized, quantized, and profiled to run natively right on your Snapdragon hardware. If you are developing advanced computer vision applications or just want to see a massive foundation model executing seamlessly at the edge, it is worth a look. Ready to compile and deploy. See model performance metrics, supported devices and more details: https://lnkd.in/gyr_CyuN #EdgeAI #QualcommAIHub #ComputerVision #MachineLearning #Snapdragon

Around 45,000 cyclists are injured in collisions with cars every year in the US. Most of those drivers didn't see them coming. Manivannan Sivan, a cyclist himself, decided to do something about it, starting with the communication problem. Cyclists signal turns with hand gestures, but most drivers don't recognize them. And taking your hand off the handlebars at speed is its own hazard. His fix: a smart helmet that listens for voice commands, "left" or "right", and triggers LED turn signals on the back of the helmet. No hands required, no fumbling, no guessing. The whole thing runs on an Arduino Portenta H7, built and trained using Edge Impulse. A voice model trained on his own audio, just "left," "right," and ambient road noise, ended up hitting 84% accuracy on testing data, enough for a working prototype. The model is small enough to run on the helmet hardware itself, with no connectivity needed. What's interesting here isn't just the safety application, it's the workflow. Dataset collection directly from the hardware, MFCC audio preprocessing, a neural network classifier, deployed as an Arduino library. Start to finish, on a microcontroller small enough to wear on your head. Edge Impulse (now part of Qualcomm) was built exactly for this kind of use case, shrinking AI down to fit the hardware, not the other way around. Read more about it here: https://lnkd.in/g246d_9G #EdgeAI #EdgeImpulse #Qualcomm #OnDeviceAI #TinyML #WearableTech

Heads up! If you're joining our Deep Dive workshop next week. We are meeting at the SEMI Innovation Center in Milpitas, not the main Convention Center in Santa Clara. It is a quick 15-minute drive from the main Summit floor. Parking in the surrounding lot is free, so you can skip the parking hassles. We are spending three hours breaking down exactly how to get heavy AI models running fast on edge hardware. Bring your projects and your questions. Whether you're deploying your first model or trying to shave milliseconds off your inference time, you will walk away with real, usable tactics. 📍 SEMI Innovation Center | 673 S Milpitas Blvd, Milpitas, CA 📅 May 13, 2026 | 9:00 AM – 12:00 PM 🎟️ $25 (Add to any Summit pass) Grab your spot here: https://lnkd.in/eT7ReB5E

GoPro added AI capabilities to their mobile app using Qualcomm AI Hub. GoPro built their reputation on capturing moments. The new challenge was making those moments easier to work with after the fact. Their team used Qualcomm AI Hub to bring real-time object tracking into the Quik app, running directly on the device, no cloud round-trip required. The result is smoother editing and better performance for the people using the app. Qualcomm AI Hub gave the GoPro team a way to optimize, validate, and deploy their model on real Qualcomm hardware without the usual friction. #QualcommAIHub #EdgeAI #OnDeviceAI #GoPro #MobileAI #Snapdragon

Usually, local object detection forces a tough choice: 1. A lightweight model that misses half the objects. 2. A heavy model that consumes your hardware. Detectron2 is incredible at drawing precise bounding boxes and tracking multiple objects at once. But running it locally usually requires massive GPU power 👉 Not anymore. A fully optimized version of Detectron2 is now on Qualcomm AI Hub. The model files are pre-exported and tuned to run natively right on Qualcomm devices. For your workflow, this means dropping high end, real time object detection into your next robotics or offline smart home build without relying on the cloud, and without melting your board. Grab the ready to run files right here: https://lnkd.in/eChbhYw5 #MakerCommunity #EdgeAI #ComputerVision #MachineLearning #QualcommAIHub

We are gearing up for Embedded Vision Summit 2026, and we just dropped a massive new interview (hosted by the Edge AI and Vision Alliance) where Meghan Stronach breaks down exactly how we are fixing the deployment bottleneck for edge developers: ⚙️ The exact, articulate route for integrating LiteRT and ONNXRT on Snapdragon. ⚙️ Moving past partial emulation to fully native, hardware-accelerated inference. ⚙️ Real debugging solutions for those incredibly frustrating runtime parsing errors. The ecosystem is shifting. The days of wrestling with fragmented documentation to get your models on-device are ending. Watch the full interview to see exactly what we are bringing to Embedded Vision Summit 2026: https://lnkd.in/e-H4fkx7 Coming to EVS in 2 weeks? Register for the deep dive session, today: https://lnkd.in/gdNki56w Who is currently building local LLMs or vision models at the edge? Drop your current tech stack in the comments! 👇 #QualcommAIHub #EdgeAI #Snapdragon #ONNX #MachineLearning #EmbeddedVision