I'm not expecting anything crazy like 8gb or anything like that, but given how many boards sell at ~50$ with 4GB of ram this just seems kinda limited.
AM57xx SoC have 2 different DDR3 memory controllers called the EMIFs. Each one has a 32 bit data width and can support up to 2GB of memory attached. A single x16 (16 bit wide data path) DDR3 chip in 512MB (4Gb) size is reasonably cheap to buy today but doubling that size to 1GB (8Gb) makes the price go up like 4x since there's just not much volume in that size sold. In order to fit onto the BeagleBone board size, only 2 DDR3 chips could fit, so only 1 EMIF is used, and to keep costs reasonable they used 512MB (4Gb) sized chips leading to 1GB of total DDR3.
Related, the C66 DSPs and M4 cores inside of AM57xx are only able to access the EMIF interfaces over the L3_MAIN core interconnect within the SoC. L3_MAIN is a 32 bit address bus interconnect, so at most can address only 4GB of memory address space. TI's memory maps for the DSPs and M4 cores both start the EMIF addressing at 0x8000_0000 so at most the DSPs and M4 cores can only access up to 2GB of DDR3. Addresses below 0x8000_0000 are mostly memory mapped peripherals, like the GPIOs and PCIe interfaces and such. The Cortex-A15 cores are able to use an extended addressing mode and can access the EMIFs through a different interface, they don't have to use L3_MAIN (but can, but there's errata), so if you connect more than 2GB of DDR3 to AM57xx then the big Cortex-A15s can access it but no one else can, which makes it less useful/valuable for a lot of designs.
If you want a dev board with more DDR3 and AM57xx, the BeagleBoard-x15 is an option although it appears to be hard to buy just the x15 from most distributors right now as they're out of stock. TI still sells the AM572x dev kit with the LCD screen directly (https://www.ti.com/store/ti/en/p/product/?p=TMDSEVM572X), albeit for a bit more than just the x15 used to cost and quite a lot more than the new AI costs.
The BeagleBone AI is aimed at prototyping in industrial automation applications. I've never worked in that area, but I wouldn't be at all surprised to see that large amounts of RAM isn't a priority for industrial controllers. Probably the software tends to be frugal with memory, because a bigger heap means more cache misses, and more cache misses mean worse latency.
A Raspberry Pi, by contrast, is mostly targeted at running a GUI and memory-hungry user applications up to and including Minecraft. It's meant for teaching kids to program and hobby stuff. It doesn't have built-in DSPs and programmable real-time units, because those are for supporting applications that fall far outside its intended purpose of having fun with Python.
The size of that model will be determined by the number of weights used. Being that industrial automation will likely use CV, that will mean the potential for a lot of weights.
Same goes for any other non trivial use case.
So yeah, 1GB is paltry.
And if you can get away with running an SVM on a 1-megapixel black and white image, then your weights will fit in 1GB with an order of magnitude to spare.
There's no reason to use an SVM over a (C)NN nowadays though.
I'd absolutely focus on ANNs if I were an academic researcher, because that's the hot flavor of the month that's going to get your career the attention it needs to bring in funding, jobs, etc. I'd also pick it for Kaggle-type stuff, where there's effectively no real penalty for generalizing poorly. Bonus points if you consume more energy to train your model than Calgary does to stay warm in the winter.
In a business setting, though, I would only default to ANNs if it were holistically the best option for the problem domain. By "holistically" I mean, "there's more to it than chasing F1 scores at all costs." The business considerations that caused Netflix to never try productionizing the prize-winning recommendation engine, for example, are always worth thinking about. Personally, I'm disinclined to look past linear models - not even as far as kernel methods - without strong reason to believe that I'm dealing with a curve that can't be straightened with a simple feature transformation. Complexity is expensive, and needless complexity is a form of technical debt.
Huh? SVMs don't perform better than NNs on less training data.
I'm sorry, but the rest of what you said is out of date and wrong. CNNs work better than SVMs for CV tasks. There's no reason to use SVMs anymore for CV, and no one in their right mind does.
That is never going to be a fair comparison.
If these are meant to be nodes in a larger system I guess that makes a little more sense, but if they're meant to be more autonomous that 1GB is going to be a real limitation for certain applications.
I expect $100 board to have 2 or 4 GB.
Yet once upon a time I thought a 48 K Apple II was a lot.