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.
...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:
Training on a dev board should be a last resort.
Even hobbyists can afford to rent gpus for training on vast.ai or emrys
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.
I've noticed this difference in quality perf in my own experiments tpu vs gaming gpu, but don't know for sure what the cause is. I never did notice a difference between gaming gpu trained models and quadro trained modela. Have more info/links?
But I would not recommend the 2GB version. The 4GB versions is barely useful without a swap file on a SSD.
This is why I would never recommend this SCB to anyone. Beside being locked to tensorflow, you are also at merci of some random manager at Google.
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.
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 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; 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, which would be a new potentially interesting state of affairs.
Dietpi also supports the N2, which is very similar to the N2+.
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.
Check out the Level Up series for getting started: https://www.youtube.com/watch?v=-RpNI4ZrfIM
And here's a fun real-life application: https://www.youtube.com/watch?v=jIyM_qT9RZw
Useful for robotics/drones, surveillance cameras, vehicles, consumer devices etc.
Is my knowledge outdated or are MediaTek SoC generally a pain to work with in GNU/Linux ?
Any information whether this SoC is supported by mainline Linux or if support will be merged in mainline Linux?
That is very important imho.
I don't know why the Coral folks bother with the dev boards; I think their USB accelerators and M.2 cards are better buys. The Pi4 + USB accelerator + a SD card costs roughly the same as this dev board and is more powerful with better community support.
I'm hoping they release a newer, faster version of the Edge TPU itself. There are some newer, faster AI accelerators from other vendors (the Gyrfalcon Lightspeeur 2803S, the Hailo-8) but the Coral USB Accelerator is reasonably priced, actually available to hobbyists, (as of recently) has open-source driver for TensorFlow, etc.
Note: I work for Google, but I don't have any inside knowledge about Coral.
I'll never do it.
Google has screwed their biggest fans too many times and we aren't Apple drones that can be pushed around.
>> $70 price difference between this and a Raspberry Pi (or similar) with much more normal processing power
> AI/ML accelerator – Google Edge TPU coprocessor with up to 4 TOPS as part of Coral Accelerator Module
Maybe this will make a difference in the specialized AI/ML workloads ?
Seems like a total loser compared to the NVidia Jetson line. It does have a few more TOPs than the Nano, I guess? And it costs a bit less than the Xavier series?
NV hardware with dedicated AI starts with Xavier, with the NX having 21 TOPs at $399 and 32 TOPs for the AGX at $699... that might leave a space for Google Edge TPUs.
* The Nano's 0.5 TFLOP of CUDA is insufficient; AND
* Google's 4 TOP of DL performance IS sufficient; BUT
* You're not bottlenecked by things like USB2; AND
* You can't afford a higher-end Jetson with dedicated ML
Well, let me just say I can't think of one on things I've actually done. I could come up with ones in abstract, but it seems like Google is aiming for 1% of the market at best. With 1% of the market, NVidia will win on community, tooling, R&D budget for v2, etc., so in the end, that means 0.1% of the market.
And that's if the two businesses were starting on even footing. They're not. Google has a horrible reputation for leaving customers high-and-dry, while NVidia's reputation is pretty good for B2B. Even if the boards were equivalent, most would pick NVidia based on that alone. Plus, NVidia is coming in with established users; they have a first-comer advantaged.
Getting late to the game with a generally inferior product with backing from a less reliable company? It seems like a loser to me.
Is 110x110mm a standard form factor? Are there cases available that could fit one or two 3.5" HDDs?
Almost all NVMe SSDs are key M.
I'm more curious if the community will be able to keep the project alive or if Google is going to keep the project theirs.
so yeah, it's a cool product that came out. give it a whirl but don't rely on it.
It was also pretty easy to setup K3s and work through any issues thanks to the Pi community activity.
Does anyone make Beowulf clusters of these? I could see that being more cost-effective if you're resource-limited even by what higher end GPUs can do.
Unless you're building something with space and/or power constraints you are much better off with a laptop or desktop.
Note that the Cortex-A35 is a CPU tier _below_ the A53 (80% of the perf at 32% lower power).