Analog hardware can generate images 100x more efficiently than digital systems, but requires rethinking model design to match fixed physics-based dynamics rather than flexible neural networks.
This paper proposes Analog Interaction Systems (AIS), a framework for building generative models on analog hardware like coupled oscillators. The key innovation is bridging the gap between what neural networks can do and what analog physics naturally computes—using time-varying parameters and hidden states to improve expressivity while keeping energy costs 100x lower than digital approaches.