Un-0: Generating Images with Coupled Oscillators
Key takeaways
- Executing deep neural networks on GPUs has dominated AI for a decade, but we think the next jump in energy efficiency demands a fundamentally different computer, one where physics does the computing.
- At Unconventional AI, we re building a new kind of computer, one that harnesses the laws of physics to do the computing.
- The best AI models today are conventional deep networks with transformer backbones.
TL;DR. Executing deep neural networks on GPUs has dominated AI for a decade, but we think the next jump in energy efficiency demands a fundamentally different computer, one where physics does the computing. We built Un-0, an image generator powered by a simulated system of coupled oscillators, an example of an emerging physical computing substrate. On Image Net 64 64 it reaches FID 6.74, matching the quality of leading conventional image generation methods when they were first published. Weights, training, and ablation code are all open. Join us on an Unconventional journey!
At Unconventional AI, we re building a new kind of computer, one that harnesses the laws of physics to do the computing. Our goal is to run modern AI on a fraction of the energy today s machines need, around 1,000x less. As a first step, we ask: can we train a physical dynamical system to generate images at scale?
The best AI models today are conventional deep networks with transformer backbones. However, there is also a long history of alternatives that seek energy efficiency by leveraging the dynamics of a physical system, such as the noisy, time-varying behavior of analog circuits that compute with analog voltage and current instead of conventional digitized numbers.