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

The Bottom Line: If you're running Frigate or any local NVR software on a Raspberry Pi, stop using your CPU for detection and buy this. It transforms slow, laggy "motion" alerts into near-instant "person" or "car" notifications. The Game Changer: Instant Detection: Before this, my Raspberry Pi struggled to keep up with camera streams. Now, object detection is lightning-fast (usually under 10ms inference time). CPU Lifesaver: It offloads all the heavy lifting from the Pi’s processor. My CPU usage dropped from 60–80% down to a cool 10–15% because the TPU handles the AI. Low Power, High Gain: For a device that adds this much "brainpower," it draws very little current. It runs perfectly fine off the Pi’s USB 3.0 port without needing an external power supply in my setup. Privacy First: I love that all my camera analysis happens locally in my house—nothing is being sent to a cloud server in another country. Pro-Tips for Setup: Use USB 3.0: Make sure you plug it into the blue USB ports on the Pi 4 or 5. It needs that bandwidth to perform at its peak. Heat: It can get a little warm during heavy use, so make sure your Pi case has decent airflow. Home Assistant: It’s basically "plug and play" once you add the Coral drivers to your config. If you aren't using Frigate with this yet, you're missing out! The Verdict: It’s getting harder to find these in stock, so if you see one, grab it. It is the single best upgrade you can make for a smart home security system.

This is a powerful device. I currently have 5 cameras running inference @ 4Hz and I'm using 12-17% of it's capacity. Be aware that you are unlikely to get it to run on Windows, it needs Linux. You will also encounter a lot of software version issues, so be prepared to put a fair bit of time into developing for it. It's worth it though, this thing really delivers!

A great processor for CCTV processing. I use this with the free Frigate CCTV system linked to 6 Reolink cameras. It plugs into the USB port on the PC and is powered through that. Easily detected by Frigate which then sends it the code to set it up so all very simple. It then offloads image processing to this device so person / car detection doesn't flog the PC and is more reliable. Not a cheap device but performs well.

Like 99% of other reviewers, I used the Coral TPU USB with Frigate to offload object inference from the CPU. This it does very well. Amazing that such a small, low cost device can do this but it goes to show how purpose built hardware can be remarkably efficicient at a specific task. I only have a couple of cameras at the moment and the device does not even get warm. Inference speed is slightly disappointing (30ms), but I put this down to the older PC it is running on. EDIT: Switching from USB2 to USB3 port brought inference speed to 8.5ms) Now for the negatives. The device changes USB ID once initialised. This can make virtualisation more difficult or less secure. There seem to be very few applications that make drop-in use of these coral devices. The device is sold as a devloper board, so possibly risks becoming another Google abandon-ware project. Still, it was quite an eye-opener to see what this little device can achieve when compared to the cpu grunt required to do the same. Currently at 65 quid, it's a far more economical proposition than it was a year ago.

Simple plug and play, got recognised immediately and definitely speeds up the AI stuff on QNAP, primarily the TS262 as the TS433 has a Neural Processing Unit built in, but i tested it anyway and it worked. I am now tempted to buy a couple more.

Kept on disconnecting from usb, tried dedicated power and settings. Do yourself a favour and get a pcie card version instead

For anyone running Frigate on their NAS - this is your missing piece. My Synology DS423+ was struggling like a hamster on a wheel trying to handle person detection, until this compact powerhouse entered the chat. Now it spots humans with scary accuracy while barely breaking a sweat. Initial setup took some tinkering (as all good things do), but the performance difference is night and day. CPU usage plummeted from "small datacenter" to "practically idle" on my NAS.

Setting this up to use for my CCTV setup in Linux was rather a pain, having to mess about with old versions of Python and such. The instructions were far out of date and had to go hunting round for guides and files. That being said it is good at its job and takes a lot of the load off the laptop I use an 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.

Genial ha bajado el uso de la cpu del pc evitando los cuegues de frigate y minimizando las falsas detecciones

Super produit qui va sur mon mini serveur sur lequel tourne Frigate NVR, il prend toute la charge de détection des objets et soulage le CPU à moindre frais Un peu compliqué à installer sous linux quand on a pas de Debian, je suis sous mageia, mais je laisse 5 étoiles quand même

Recently ditched motioneye for Frigate. Frigate is pretty powerful, but takes a toll on the processor. This "coprocesssor" speeds up detection and recognition. Works well, I would buy again.

Good