Un-0: Generating Images with Coupled Oscillators

https://unconv.ai/blog/introducing-un-0-generating-images-with-coupled-oscillators/

by babelfish • 27 days ago

191 53 27 days ago

unconv.ai

mrr7337• 27 days ago

I didn't really understand anything...lgtm

fusionadvocate• 27 days ago

Is this somewhat related to reservoir computing?

fc417fc8027• 27 days ago

(Disclaimer, not my area of expertise.) It appears to be adjacent but more general. There's an entire collection of methods (including reservoir computing) that conceptually resemble or are based on physical systems in one way or another. This appears to be an attempt to develop a new method that natively takes place as a physical process that we could readily implement in hardware.

andybak• 27 days ago

When I first learned about computer science at the age of 11 or so (and in 1982 or so) the first page of the text book put digital and analogue computers on what seemed to be an equal footing. And then proceeded to ignore the latter for the rest of the book. Apart from a few notable exceptions ( https://en.wikipedia.org/wiki/Phillips_Machine ) I've often wondered about analogue computing.

seanmcdirmid• 27 days ago

At the end of my undergrad, I remember a UW professor being poached by intel to work on analogue computing research project, the chair of the department at the time said that it was an opportunity that might not ever happen again and he had to take. I don’t think it went anywhere (since I never heard of intel coming out with a product), but I at least knew there was an attempt.

em3rgent0rdr• 27 days ago

Noise and component imprecision has always limited analog computing.

PaulHoule• 27 days ago

And a general lack of reconfigurability to solve general problems. There’s been interest in analog neural networks for a long time.

Those problems you mention are important in music synthesis where people could live with limited reconfigurability but reliability is at a premium: synth players in early touring bands (e.g. Yes) had to be electronics technicians and instruments have to survive being packed in boxes and transported everywhere. The Yamaha DX-7 made FM synthesis mainstream because digital FM synthesis was absolutely reliable.

vintermann• 27 days ago

In neutral networks, we seem to be pushing towards ever lower precision floats, and we use noise for all sorts of useful things.

vessenes3• 27 days ago

Also true: all computing is analog computing.

mikestorrent• 27 days ago

Quantum annealers (D-Wave machines) are basically analogue computers, with Josephson junctions as the primary component as opposed to oscillators. I wonder if they could render these images, too?

jcims• 27 days ago

Take a look at extropic. AFAICT it's a form of analog computer.

mdnahas• 26 days ago

My father designed processors. He says all electronics are analog. Some just pretends to act digital.

jerf2• 26 days ago

If you want to understand the issue with analog computers, design a SHA-256 circuit for one of them and consider the consequences of trying to push a megabyte of data through it. While that is an extreme example I chose precisely to make the issues clear, much real computing has many of the same characteristics, just distributed a bit more widely in time and space.

Or, to put it another way, you can make anything sound good if you consider only the positives and anything sound bad if you only consider the negatives. Analog computing sounds amazing when you read the brochure and consider only the positives. But when you bring the negatives back in, it makes sense why it is not frequently used. It is not a case of the mainstream keeping some great idea down because, uh, Big Digital or something, it's a case where digital computing turns out to be a stonking good idea and it's hard for the analog world to compete and it's virtually impossible for them to ever be anything but a niche.

Neural networks are an interesting possibility for a future successful niche, although even so, it would be neural networks specifically that may grow in importance and not analog computing in general. And I still wouldn't guarantee it'll be a good idea... we may have a lot of trouble keeping what would be very deeply nested analog circuitry stable in the real world and digital may still win out, e.g., an analog neural net that has a noticeable personality shift when it gets warmer may not be the best engineering solution. That's a question for 20 or 30 years from now.

ShinyLeftPad• 26 days ago

Is SHA-256 something that makes sense in analog realm, or is it something that only needs to exist due to digital constraints?

NopIdoN• 27 days ago

However, the trade-off with our approach is that it requires a more complex loss that operates given only generated samples.

OutOfHere• 27 days ago

Can this even make an image having more than one "class"? Can it make an image of an astronaut riding a horse on the moon?

vessenes3• 27 days ago

Yes, I had the same question. I don’t think so, as currently designed. It trains to specific points / classes in an embedding space. They didn’t discuss how one might go to non-trained points in the paper as far as I could read, and they did show some visualization around the idea that the runs aim at / around set points in the space.

ainch• 27 days ago

This method is cool and the post explains it well. It would, however, be good to get more detail on the energy efficiency they flag as their motivation: is this model actually more energy efficient than the comparators they highlight?

fc417fc8027• 27 days ago

It seems like total parameter count is more or less on par with conventional approaches so any gains won't be from there.

We can implement coupled oscillators in hardware but are the couplings and frequencies programmable? If they're being streamed in I guess you'd still have a memory bandwidth bottleneck and associated energy usage. If not then the fair comparison is to a conventional model hardcoded in an ASIC which AFAIU is actually quite energy efficient.

alfiedotwtf2• 26 days ago

Do the parameters in these harmonic systems compress better? Instead of needing to hold individual parameters for each oscillator, could groupings of oscillators be instead be described with its output over a given time and then just reverse that output to get the original parameters (I’m thinking the output is like an FFT of the oscillators which is a single value, then do an inverse FFT to get the original oscillator parameters etc)

TaupeRanger• 27 days ago

Really interesting - if I understood the article correctly, they're simulating this on conventional hardware, so in order to get the proposed benefits, it would need to be implemented in some other electronic medium.

italiansolider• 27 days ago

Readers care, this requires a nice amount of physics knowledge to really understand. Not too advanced but still, physics.

WhitneyLand3• 27 days 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.

ttul2• 27 days ago

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!

ttul2• 27 days ago

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.

fluoridation• 27 days ago

Doesn't that require quadratically-many wires to connect all the processing units?

anigbrowl• 27 days ago

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.

itishappy2• 27 days ago

I bet 5 million coupled oscillators, all slightly detuned, would sound freakin' amazing.

fc417fc8027• 27 days ago

The oscillating elements don't map directly to pixels. Conventional models also have n^2 parameters.

WhitneyLand3• 27 days ago

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?

the8472• 27 days ago

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.

kannanvijayan• 26 days ago

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.

foax• 27 days ago

This kind of reminds me of DCT in lossy image compression, but in reverse.

dimatura• 27 days ago

Very cool work - refreshing to see a of different approach. I learned about Kuramoto oscillators many years ago from a book called Sync, by Steven Strogatz, which I highly recommend.

_def• 27 days ago

Not at all related but still reminds me a bit of FM synthesis

luciana1u• 27 days ago

finally, a way to generate images that's slower AND worse. progress.

tugdual• 27 days ago

I love this ! Used to work at Rain AI on training neural networks in unconventional hardware - people often that computers don't necessarily have to be electronic digital - there is a whole domain dedicated to creating machines that can apply certain mathematical operations faster or more efficiently than their electronic counter parts. I created this site to try create a classification of that space:

https://computers.tugdual.fr/

vessenes3• 27 days ago

Very cool. I’m reminded of Wolfram’s pitch that neural nets are a search through the very broad computational complexity of the function space they describe; he did a little work to show that you could find similar behavior in other function spaces. These oscillators are yet again a different function space, and its cool they can be harnessed in this way.

The question of what physical / electronic phenomena is the most efficient yet large enough function space to be used for inference is a really good one to think about. I have no suggestions.

pizzao• 26 days ago

The required compute seems a bit high: "We trained all CIFAR-10 models on 1xB200 GPU, and all ImageNet 64×64 models on 8xB200 GPUs. The largest CIFAR-10 model uses 20 B200 hours to train, and the largest ImageNet 64×64 model uses 640 B200 hours"

20 B200 hours for CIFAR-10 seems like a lot...

orbital-decay• 26 days ago

They're proposing a loose range of hardware that is thought to be more efficient when used with this thing, that's the entire point. What they did with B200s is just a simulation, it's not supposed to be GPU-efficient.

Un-0: Generating Images with Coupled Oscillators

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