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

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.

Works as advertised. I am running Home Assistant with Frigate on a RPi5 and frigate was using about 65% to 75% of CPU. After connecting the Coral Edge TPU USB dongle through a powered USB hub to the RPi5, the CPU use is now 4%.

Great technology that was simple to configure and launched quickly on a Raspberry Pi 4 using Docker Frigate.

The first one I received was DOA. I see that this is a recurring theme among the reviews. I'm planning to try another one, but I have my doubts. I'll update my review if the next one works correctly. Update: My second unit arrived in working condition. I feel like this is an issue with the actual manufacture of these items rather than the seller. The fact that multiple people had them arrive dead, really stinks. It makes me question how long this unit will last. Thankfully, Amazon is good with DOA returns, but if this breaks after like 45 days, I'll need to lower my rating again. That being said, I'm using this to augment my security system, and it is working quite well. It drops video identifications down from 130-140ms down to about 27ms. Basically a 4-5x speed improvement. It also takes a nice load off the CPU. I'd recommend it to augment an NVR, especially a low-powered model.

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.

Does great detection with Frigate NVR

Purchased this device from this seller after a previous order from a different seller arrived DOA. Although slightly more expensive, device arrived quickly and haven't had any issues. Using it with Frigate running in a VM on a NUC 12 Pro with 4 cameras. Device works great and performs as promised, reducing CPU usage in the NUC significantly. Would highly recommend it for this purpose. Getting the device flashed, configured, and passing through to the VM is a little tricky and outside of the scope of this review, but for others who intend to use it that way, search for William Lam's guides on this. They're very detailed, easy to follow, and will get you up and running quickly.

Unfortunately, I purchased this Coral TPU just as the AI industry was moving away from them. Now, AI generally does not use these TPUs, and I had to buy GPUs to replace them.