Comment by WhitneyLand
20 hours ago
It’s not clear to me how this would ever be practical since it seems dependent on n^2 scaling.
You’ve got to wonder when you have an image generation demo why would you possibly have 64 x 64 pixel output as your demo?
If I’m understanding this properly to generate a 4K image, you need like 5 trillion point to point connections on the chip. Even if power use from the oscillators is zero that’s going to be an issue.
Yes I too am perplexed. I'm into audio synthesis so I feel I have somewhat better-than-average knowledge of oscillators, from the component or elementary mathematical level (depending on whether they're analog or digital) to complex interactions for fun and profit (frequency, phase, ring modulation).
These are cool results but I was disappointed not to find any discussion of where oscillator array technology stands today what the manufacturing challenges/opportunities might be. It seems like it would be prohibitively expensive for anything beyond minimal networks of a few hundred nodes that could be used in sensors. Even if you have perfectly consistent oscillators that synchronize to each other within very fine tolerances, wiring them up to each other is still a massive headache.
I bet 5 million coupled oscillators, all slightly detuned, would sound freakin' amazing.
Here's 1000: https://www.youtube.com/watch?v=c3wk9WWTfNs&t=338s
What they are trying to achieve is to demonstrate that the coupling approach works in a simulated physics environment (O(n^2) as you point out) so that they can then build CMOS circuits that create actual oscillators and then let the laws of physics do the computation. This is a very bold vision!
And anyone who has done an introductory course in VLSI design would know that capacitance (coupling) is something you usually want to get rid of. However, all kinds of amazing analog circuits have been developed over the decades that exploit coupling effects. So, their idea is not outlandish at all.
But wouldn't capacitance as it naturally occurs be only to immediate neighbors? Not n^2 as in their model.
Which idea is not outlandish? Physical computing? I agree physical computing is a fascinating topic.
But specifically what they’ve simulated here? I don’t see how that would ever work in real life scaled up to any kind of real size.
I’m not criticizing them for starting out small. Lots of things can be proven with small models. I’m saying in principle, I don’t see how this will work unless there’s some fundamentally new technique that is currently not known about. Maybe they have some secret idea but they haven’t shown it here.
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Doesn't that require quadratically-many wires to connect all the processing units?
I read through the article, and I'm not sure this is dependent on quadratic scaling.
Are they allowing all oscillators to influence all others, or are they picking modalities where the influences can be limited to some maximal fixed degree?
One would imagine that there'd be a variety of different topologies available to explore. Even if during training the treatment was fully connected, one could imagine the training itself biasing towards a maximal fixed degree per oscillator, and then inference later operating on a quantized version of that that drops the low-weight influences to zero.
The oscillating elements don't map directly to pixels. Conventional models also have n^2 parameters.
Well image generators work differently…
Do you mean that they may get away with less oscillators because of the decoder layer? Well there’s the rub isn’t it, the more work you have done by a software layer the less power you’ve proportionally saved by having it be done by physical computing.
But let’s spitball here what would you estimate would be needed in number of oscillators and interconnects for a 4K image?
Conventional image generators still have to process n^2 connections so I don't think that observation is a valid objection in and of itself.
One thing I'm unclear on is that their total parameter count scales similarly to conventional models but many of those conventional models incorporate convolutions. I wonder how interconnect count (as opposed to unique parameters) compares to performance?
As to 4k images, I'm not clear how much farther their current architecture would be expected to scale. Single layer networks aren't parameter efficient compared to deep networks; I'd naively assume that to also apply here. That said given their results so far with what amounts to a single layer the naive assumption begins to seem questionable.
Think of the models making progress on CIFAR-10, ImageNet, CelebA, etc. 15 years ago. They had issues too and weren't just scaled-up as is to the architectures we have today.