-
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: https://aiyprojects.withgoogle.com/edge-tpu
#AIYProjects#EdgeTPU -
There were so many on-device ML talks at Google I/O this year! I summarized my learnings in sketchnotes.
#Tensorflow#TensorFlowLite#DanceLike#MLKit#Firebase#CameraX#EdgeTPU#FederatedLearningpic.twitter.com/Xyox9QHI3R
-
In today's mailbag, an
@ASUS Tinker Edge T board, https://www.asus.com/uk/AIoT-Industrial-Solutions/Tinker-Edge-T/ …. The first non-Google board to use the#EdgeTPU. Time for some benchmarking, although in theory there shouldn't be any performance difference between this and the Coral Dev Board, https://www.hackster.io/news/the-big-benchmarking-roundup-a561fbfe8719 ….pic.twitter.com/8bHuz7UIOK
-
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: https://coral.ai/docs/edgetpu/retrain-classification/ …
#Gravitylink#edgetpu pic.twitter.com/NAPJ5y7e3l
-
Announcing EfficientNet-EdgeTPU, a family of image classification models created using
#AutoML and optimized for use on#EdgeTPU, that provide real-time performance while achieving the accuracy of much larger, server-side models. Check it out below ↓ https://goo.gle/2MK8Vpe -
Playing with the new Coral Dev board and USB Accelerator stick
Thanks @plainconcepts!!@GoogleAI#CoralDev#AI#DeepLearning#EdgeTPU pic.twitter.com/ZSGwgXyAfA
-
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].
#EdgeTPU [1] https://www.slideshare.net/kstan2/a-peek-into-googles-edge-tpu … [2] https://android.googlesource.com/kernel/msm/+/refs/heads/android-msm-coral-4.14-android10-c2f2/drivers/misc/airbrush/ …Prikaži ovu nit -
Full house at the Computer Vision Workshop
@GDGSeattle today! tf.#Keras#OpenCV#TFLite#EdgeTPU#RaspberryPi#AndroidML pic.twitter.com/hKEXXDTyEQ
-
"The arrival of a Google Coral ecosystem," by me for
@HacksterIO. Google's Edge TPU just became a lot more interesting, https://www.hackster.io/news/the-arrival-of-a-google-coral-ecosystem-68c2a9acd68f ….#MachineLearning#EdgeComputing#EdgeTPU -
a peek into Pixel 4/4L driver binaries shows that the NN accelerator ASIC in EdgeTPU is a part of the Pixel Neural Core. https://developers.google.com/android/drivers
#EdgeTPU -
I built a real-time object tracker with
@Raspberry_Pi@TensorFlow#TFLite,@pimoroni Pan-Tilt HAT,#EdgeTPU#MobileNetV3 architecture (so fresh
)
Today I:
Released the code (pip install rpi-deep-pantilt)
Wrote a step-by-step guide
Enjoy
https://medium.com/@grepLeigh/real-time-object-tracking-with-tensorflow-raspberry-pi-and-pan-tilt-hat-2aeaef47e134 … https://twitter.com/grepLeigh/status/1199910608474263552 …pic.twitter.com/qoOgfNwarN
-
Machine Learning: Googles Mini-TPU kosten mit Board 150 US-Dollar
#edgetpu https://glm.io/139824?t -
This is the new era of embedded world with AI
#EDGETPU https://twitter.com/GCPcloud/status/1088277712186298373 … -
I add human pose estimator node in coral_usb ROS package. https://github.com/knorth55/coral_usb_ros …
#ROS#coral#edgetpu pic.twitter.com/kxAjgOLAP8 -
EdgeTPU (pcie) on NanoPC-T4 with custom armbian
#nanopc#FriendlyElec#aarch64#arm#server#ai#edgetpu#armbian#robosensing#mininodespic.twitter.com/xMjuteyvqy
-
What do you get when you combine a bicycle with
#TensorFlow Lite, an edge TPU, a camera, a#RaspberryPi and an LED strip? Safer cycling!! https://coral.withgoogle.com/@GoogleAI#edgetpu#coral#tflite#computervision#MachineLearning@BromptonBicycle@ZackAkil pic.twitter.com/1We5AK1xLj
-
Thanks to
@KevinPHolbrook from@google for showcasing#edgeTPU, first toolchain for TPU in industry.#GoogleNext19@furrier@DavidLinthicum@dvellante@kelseyhightower@digitalcloudgal@MarshaCollier@MikeQuindazzi@zehicle@zehicle@antgrasso@MS1MN@dhinchcliffepic.twitter.com/bVxy0ndUvW -
Models trained specifically for
#EdgeTPU! Notably, they use post-training quantization and only see a 0.5% drop in accuracy as a result https://twitter.com/GoogleAI/status/1158804847488978944 …
Čini se da učitavanje traje već neko vrijeme.
Twitter je možda preopterećen ili ima kratkotrajnih poteškoća u radu. Pokušajte ponovno ili potražite dodatne informacije u odjeljku Status Twittera.