USB Edge TPU ML Accelerator coprocessor for Raspberry Pi and Other Embedded Single Board Computers

This is an amazing and beastly device. If you know you know. It outperforms stacked RTX 1080s like it's child's play.I use this for a Frigate NVR that does detection on 5 camera feeds. Works great and never gets anywhere near capacity. I have it plugged into a rack server with USB 2.0 and it's fine. Probably would be superior with USB 3.Just plug it in and then follow the website. For debian it was a simple repo and package install. That's it. Ready to use. Don't even need to reboot of course yay Linux.If you use it for a Frigate setup maybe keep a light video card like a 2GB Nvidia to use for the movement detection in Frigate (instead of CPU) and use this TPU for object detection. You'll be amazed at the speed increase.

Got this for my Frigate server to help take the load off the cpu. The usb connection works with any computer with a USB port giving your motion capture and detection a boost.

I’ve been running the Google Coral USB Accelerator as part of my self-hosted Home Assistant and Frigate setup in my home lab, and it’s been a solid upgrade.My cameras stream through it for real-time object detection, and while the AI recognition isn’t perfect, it’s definitely good enough for home security and smart automation triggers.It picks up people, cars, and even the occasional animal (cats 🐱) with decent accuracy, and it’s responsive enough for live notifications or actions.The biggest win is offloading the CPU.Before Coral, my server was getting hammered by the detection workload, especially with multiple cameras running.Now, it’s smooth, CPU usage is way down, and the system feels a lot more stable and responsive. If you’re running Frigate or anything TensorFlow-based in a home setup, the Coral USB is a no-brainer.It’s compact, plug-and-play with a bit of config, and does exactly what it’s meant to.Note that the unit get pretty hot while working, this is normal.

Although rather pricey on Amazon, it's nigh impossible to get these at MSRP as they're sold out everywhere.If you need one, they work for fast inferences (depending on the machine learning algorithm you use). Faster than CPU or GPU inferences and pretty handy if you know you want to use them. Be sure to use on a supported system that has drivers that you can compile easily. For linux based systems, depending on your distro, it can be a bit of a pain to get working but a little googling goes a long way.

I got it for transcoding, but the camera worked with and without it so not sure I needed it in my situation. Only get it if you find you have problems and don't just assume you need one to record video.

Works!

This has decreased the load on my CPU considerably for HomeAssistant/Frigate. Highly recommend if you are considering it for that purpose.

I had lots of hope for this... it would have been great to have a self contained TPU solution that can provide an assist to classification and detection tasks which I normally do with OpenCV on either a CISC or GPU right now.In comes Coral. The promise of fast tensor operations using a lower power dongle can't be beat.Now comes the bad: first, good luck finding this for the MSRP. Either production is low, or scalpers are having a field day on this. So, from the get-go you're paying 10-20% premium on the device.Next, if you get your hands on it, good luck trying to get it working with anything. Lots of the reviewers like this because they utilize it with Frigate which is cool. A+ for that workload...Now if you want to use it with anything else, there are some examples... and that's where things hit a bumpy road. Check out any of the Github repositories that are posted. Most were posted 3 or 4 years ago and have been untouched since... so trying bringing down an example and getting it to work... Windows examples don't work... WSL doesn't work... a recent version or LTS on Ubuntu... and same thing... nothing works. Good luck getting a response from the support address.So... summary: this thing is great if you have Frigate and need to increase camera counts without buying more cores or GPUs. It may be great if you can get it running on a RPi and do things that are simply not possible, there... but for CV applications... getting this to run will be the long tent pole due to the poorly maintained examples and stale support repositories making it a better move to just skip over this and go with a GPU solution such as a CUDA accelerated OpenCV approach or Deepstacks or anything else for that matter.If anything changes and I get this thing functional, I'll update this review accordingly.