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

Made a big difference to the AI and image detection on my Frigate server. Simple to install, but the software can be a bit tricky

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: edgetpudevice: usb"That's all I did!Hope this helps someone.

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.

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.

One star as not working. Initially i tought that I was not able to configure it for my computer. Item returned adn Alafter read different reviews from other sellers bought another one from a different suplier as tried to buy again from amazon but price was increased with 50 £. The one ordered from different platform working. Recognised as soon as pluged it in by windows and light turned on as soon as inserted. If no light is turned on or if windows does not recognise it when pluged then it might be deffective.

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!

I work in AI and bought this expecting a general-purpose inference accelerator. It isn't one, and that's on me as much as Google, but the marketing doesn't help.What you're actually getting is a 4 TOPS coprocessor that runs int8-quantised TensorFlow Lite models and nothing else. No training. No PyTorch. Nothing that isn't through the Edge TPU compiler. If your model doesn't fit its small on-chip memory, weights get shuttled across USB on every inference and your speed-up evaporates.That's the bottleneck, and it's architectural rather than a fault. USB 3 gives you roughly 0.6 GB/s where a PCIe card gets you twenty-odd times that. You cannot feed an accelerator properly through a straw.It gets hot. Fair enough, it's working. Worth knowing that a poor cable will silently drop you to USB 2 speeds, which caps your inference rate and, confusingly, makes the thing run cooler. Cool and slow is a cable problem, not a chip problem.Where it earns its money: a Raspberry Pi or a low-power always-on box doing object detection. Frigate users swear by it and they're right to. Genuinely transformative on weak hardware at 2 watts.Where it doesn't: any machine that already has decent silicon. My MacBook's Neural Engine has close to ten times the raw throughput, on-die, no USB in the way. I bought a coprocessor for a computer that didn't need one.Also worth knowing before you buy: the software stack has stalled. Pycoral still wants Python 3.9 and Google has been archiving the repos. Expect to fight your environment.Three stars. The hardware does what it says. Just be honest with yourself about whether your host is the weak link, because if it isn't, this won't help you.

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.