https://blog.usejournal.com/google-coral-edge-tpu-vs-nvidia-...
...and Google is pretty invested in TPUs, since it uses lots of them in house.
I'm sure an ML accelerator that doesn't support training will be great for applications like mass-produced self-driving cars. But for hobbyists - the kind of people who care about the difference between a $170 dev board and a $100 dev board - being unable to train is a pretty glaring omission.
Assuming that ratio holds, you'd maybe get 231 GFLOPs for training. The Nvidia GTX 9800 that I bought in 2008 gets 432 GFLOPs according to a quick Google search.
Hobbyists don't care about power efficiency for training, so buy any GPU made in the last 12 years instead, train on your desktop, and transfer the trained model to the board.
See this paper for an example of interactive RL: https://arxiv.org/abs/1807.00412
Note: I haven't tried sourcing these in production (100k+) quantities so I have no idea what guarantees that product line gives customers.
Also edge tpu is 2-5Watts. Supposedly cloud tpus are more power efficient than GPUs, and for eg the 14 tflops 2080 ran at 300 W regularly.
A full TPU v2/v3 can train models and use 16/32 bit floats. They also have a Google-specific (?) 16-bit floating point type with reduced precision.
But I would not recommend the 2GB version. The 4GB versions is barely useful without a swap file on a SSD.
The fastest SBC at CPU tasks priced below $100 is the Raspberry Pi.
The Odroid N2+ costs $79 and is over twice as fast as the Pi4. The Khadas Vim3 costs $100 and is about 30-40% faster than the Pi4.
The number of SBC boards out there is becoming huge; although the PI price has dropped significantly wrt performance and features (especially RAM), there's a lot of comeptition, and it's growing.
https://hackerboards.com/spec-summaries/ https://all3dp.com/1/single-board-computer-raspberry-pi-alte...
That's indeed much faster than the Pi4. Do you know the state of kernel support for that board?
Except the ARM G51 Bifrost gpu, which has only recently started to see viability[2] thanks to one hacker's reverse engineering. If you want to read a lot of words, there's a status report from the libreeelec Kodi-based media player distribution distribution that's a year old, that lays out a lot of what needs be done, from a very video-intense perspective[3]; this is before recent reverse engineering efforts, & largely discusses uses closed proprietary blobs, but still interesting. Most recently & very interestingly, there are signs that ARM itself may be willing to start helping out the reverse engineered development[4], which would be a new potentially interesting state of affairs.
[2] https://www.phoronix.com/scan.php?page=news_item&px=Bifrost-...
[1] https://forum.libreelec.tv/thread/21134-what-aspects-of-hard...
[4] https://www.phoronix.com/scan.php?page=news_item&px=Arm-Panf...
https://www.armbian.com/odroid-n2/ https://forum.armbian.com/search/?q=odroid%20n2%2B&fromCSE=1
https://wiki.odroid.com/getting_started/os_installation_guid...
Dietpi also supports the N2, which is very similar to the N2+. https://dietpi.com/
The H2+ has an Intel J4115 Atom celeron running 2.3GHz all-core, which I expect would trounce these ARM chips. It's also $120.
Alas there hasn't been any update to the excellent Exynos5422 that started HardKernel's/Odroid's ascent as the XU4. Lovely 2GHz Cortex 4x A15 4x A7 with (2x! wow! thanks!) USB3 root hosts and on-package RAM: really an amazing chip way ahead of it's time. These days it's way outgunned but this chip really lead the way for SBCs with it's bigger cores for the time, USB3, and on-package RAM (which we really need to see a comeback on).
Worth noting that the A73 on the N2/N2+ and RPi4 are from ARM Artemis, which hails from 2016. Maybe some year SBC won't all be running half decade old architectures, but at least we're at the point where half a decade ago we were doing something right. ;) Still, one can't help but imagine what a wonder it would be if an chip & SBC were to launch with an ARM X1 chip available.
the attraction of coral is supposed to be the inference engine. 4 TOps/s at 2 watts is... impressive. Jetson takes 10 or 15 watts & tops out a little under 0.5 TOp/s. those are much more flexible gpu cores but that's 60x efficiency gain & centered around a chip that is much easier to integrate into consumer products.
Xavier NX is 21TOPs at 15W for the whole SoC... but the pricing at $399 puts it in a different category...
Google should just start selling the USB sticks at the same price as the M.2 Corals, with them being used on RPis I think...
Jetson has obsolete distros though? Linux support is probably better with Google if anything.
Jetson is actually an important product for Nvidia and Google tends to kill off this type of pet project.
Google/alphabet might have more success with their side-bets if they spun them out as separate companies like Xiaomi and Haier (both Chinese) seem to do.