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.

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.

The included USB cable had connection issues but using it with a known working cable has it working perfectly. I am using it in CodeProject.AI with AgentDVR for AI detection. I had to disable it from going to sleep because it wouldn't wake up properly.

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!

Honestly, this feels like abandoned hardware. The Coral USB TPU itself is fast and interesting technically, but the software support is a mess in 2026. Many official guides are outdated, dependencies break on modern systems, and getting it working properly on Raspberry Pi 5 becomes more of a debugging project than actual AI development. Google barely updated the Coral ecosystem for years, so you spend more time fighting compatibility issues than building anything useful. Great idea, disappointing long-term support.

Used on RPi5, running Haos and Frigate. I was a little worried about this item because of the large number of 1 star reviews with detailed comment about lack of support and setup issues, and, Amazon's warning of "frequently returned item". However, plugged it in, put detector settings in conf.yaml and off it went after restarting Frigate. The dmesg/lsusb info changed after restarting as shown elsewhere. Inference stats now down at 9mS. I'm a little wary of putting comments here that might be lacking in some way as I've still a lot to learn about this stuff. However I was pleasantly surprised and impressed that it worked so well, so quickly with no debugging. Note the comments on the datasheet that it can get quite warm when working. In case it helps anyone (took a little searching on the net for me), the 'detectors' entry immediately before camera definitions I use is: "detectors: coral: type: edgetpu device: usb" That's all I did! Hope this helps someone.

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.

Just worked.