What are the use-cases for a GPU-accelerated single board computer?
Note that embedded radios / modems / codec manipulation is incredibly common. Usually its handled by a DSP (VLIW architecture), but I'd imagine that the GPU architecture has more FLOPs. DSPs probably have less latency, but something like SDR is not very latency sensitive (or more like: the community models latency as a phase-shift and is therefore resilient to many forms of latency)
Note: I'm not actually in any of these communities. I'm just curious and someone else told me about this very same question.
It's pretty neat stuff to think about DSP on hundreds of MHz of bandwidth being something that you can do in software these days. I remember when that was firmly in the realm of "don't bother unless you want to design a custom ASIC" and now it's starting to become even hobbyist-accessible.
The modern day has brought incredible advancements to the modern hobbyist. Just 10 years ago, you probably still needed to play with OpAmps and Inductors to effectively read a hundreds-of-MHz signal in any real capacity (at the hobbyist scale at least: $1000 or less projects). A PC probably had the compute girth back then to interpret the data, but you still needed a high-speed, and high-quality, ADC to even read the data that quickly.
Is this a developing markets play? I've never heard of someone whose only computer is a Pi/Jetson. By the time you add all the necessary peripherals to use it as your only computer, it costs more than a cheap laptop.
So the point of a Jetson is .. you only have a cheap laptop with terrible integrated graphics, and for $60 you get a much better GPU with this Jetson, and you remote into it?
I think it'll do better in gaming.
Compared to him testing on a Raspberry Pi 4: https://www.youtube.com/watch?v=l4TyYU9Xhcs
(note both those videos are a old so results could be different now)
The video is a year old, but I imagine there would be less bugs now. With this cheaper board coming out, there might be a bit more of development activity.
This Jetson really isn't a platform to run emulated games - for that there are much better boards, with more RAM. Which is a lot more important issue. You can't do miracles with 2GB of RAM.
Jetson is made for computer vision, neural networks and signal processing, where the GPU isn't used as a GPU but as a massively parallel co-processor to crunch through a lot of data at once.
No, not even close. It's embedded systems with GPU acceleration, mostly for machine learning.
(strictly speaking any screen with higher resolution and/or expectations of smooth non-trivial graphics requires some kind of GPU, but I take the question to mean why one would want a powerful one)
As someone who has tried to run a desktop environment on various Pi-like devices for years, with every generation being "the one that will replace your PC" I just laugh.
The modern web is nearly unusable with less than 4 GB of RAM and it's really easy to push beyond that. I personally would not recommend that anyone try to use anything with less than 8 GB of RAM as a general purpose desktop computer anymore.
You can do it, sure, but there will be significant compromises and limitations along the way. A secondhand x86 PC with 8+ GB of RAM is almost always a better choice for general purpose desktop use. Leave the "hacker boards" for roles that benefit more from their compact size, low power consumption, and/or specialty I/O capabilities.
I haven't tried the Pi 8GB, that should at least solve the RAM problem, but the lack of native high-speed storage still likely impacts usability. Here's hoping the major Pi-likes follow suit to offer variants with more memory soon.
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And of course as noted by other comments, another major reason to think of these things more as appliances is the generally spotty support for updating kernels and the like that is unfortunately standard in the ARM world. That entire side of the industry is infected with NDA culture and has no interest in curing it.
If you price a silent PC with a fanless NVidia card it's quite a lot more.
More specific; What is the use case of a battery enabled robot “brain” with machine learning?
I'm hoping someday we can get something like mister on the pano logic boards. You can find 2nd gen ones on ebay for $15 or so and have usb, dvi, audio, and I believe a more powerful fpga. All in a solid metal case.
But more seriously: robotics.
Only the MAIX platform has a price advantage over this now, and the software is much less mature.
That being said, programming the Maix is a piece of cake. The Jetson Nano was very challenging to deal with; the software is unstable in many cases, and the levels of abstraction are all mixed up, and discovering which Nvidia tools to use is an absolute pain (Gstreamer vs DeepStream vs ARGUS vs whatever else Nvidia pumps out this year). Also, I had way better support from the Maix community than from Nvidia. Nvidia's support model seems to be "unless you're spending tens of thousands of dollars, go pound sand, maybe a volunteer on our forums will help you, if anyone knows what's actually going on here."
Nvidia just has too much software, too little documentation, and holds onto their secret sauce so tightly that doing anything is a true pain.
OTOH, the MAIX has things like the pin assignments wrong[1] and you need a magic (undocumented) flag to make the LCD display colors correctly. There's also different types of models supported with undocumented constraints on each one, and you need to find the correct set of random open source projects to make them work. But it is an exciting platform!