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.
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.
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?
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.
Not at all related but still reminds me a bit of FM synthesis
This kind of reminds me of DCT in lossy image compression, but in reverse.
Readers care, this requires a nice amount of physics knowledge to really understand. Not too advanced but still, physics.
> However, the trade-off with our approach is that it requires a more complex loss that operates given only generated samples.
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?
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.