Rezultati pretraživanja
  1. 25. srp 2018.

    One of the cornerstones of privacy-preserving machine learning is the ability to run models on a chip in situ, such that the data don't need to go anywhere. Not all AI should run in the cloud! Proud our AI chip is now public:

  2. 14. svi 2019.

    There were so many on-device ML talks at Google I/O this year! I summarized my learnings in sketchnotes.

  3. In today's mailbag, an Tinker Edge T board, . The first non-Google board to use the . Time for some benchmarking, although in theory there shouldn't be any performance difference between this and the Coral Dev Board, .

  4. prije 11 sati

    ✅Totorial: How to retrain an image classification model? 👉You'll use a technique called transfer learning to retrain an existing model and then compile it to run on an Edge TPU device details:

  5. 6. kol 2019.

    Announcing EfficientNet-EdgeTPU, a family of image classification models created using and optimized for use on , that provide real-time performance while achieving the accuracy of much larger, server-side models. Check it out below ↓

  6. 28. tra 2019.

    Playing with the new Coral Dev board and USB Accelerator stick🙈 Thanks !!

  7. 28. stu 2019.

    The device driver of the EdgeTPU in Pixel 4 is different from the Dev board one (apex). The apex, as I noted couple months ago [1] was upstreamed. This Pixel 4 one is packaged in with IPU, DRAM and others [2]. [1] [2]

    Prikaži ovu nit
  8. 8. lip 2019.

    Full house at the Computer Vision Workshop today! tf.

  9. 3. sij

    "The arrival of a Google Coral ecosystem," by me for . Google's Edge TPU just became a lot more interesting, .

  10. 26. lis 2019.

    a peek into Pixel 4/4L driver binaries shows that the NN accelerator ASIC in EdgeTPU is a part of the Pixel Neural Core.

  11. 23. lis 2019.
  12. 8. pro 2019.

    I built a real-time object tracker with , Pan-Tilt HAT, architecture (so fresh 🔥) Today I: ✅Released the code (pip install rpi-deep-pantilt) ✅Wrote a step-by-step guide ✨Enjoy✨

  13. 6. ožu 2019.

    Machine Learning: Googles Mini-TPU kosten mit Board 150 US-Dollar

  14. This is the new era of embedded world with AI

  15. 26. lis 2019.

    I add human pose estimator node in coral_usb ROS package.

  16. 15. sij
  17. 14. ožu 2019.
  18. 26. ruj 2019.

    What do you get when you combine a bicycle with Lite, an edge TPU, a camera, a and an LED strip? Safer cycling!!

  19. 11. tra 2019.
  20. 6. kol 2019.

    Models trained specifically for ! Notably, they use post-training quantization and only see a 0.5% drop in accuracy as a result

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