Scalable physical deep learning using optical dynamics with state-skipping training and single-map architecture

Escalating energy demands for high-performance machine learning have sparked growing interest in unconventional computing paradigms rooted in physical systems. Central to this direction is leveraging physical properties to design computing frameworks integrating strong learning capabilities, efficient training strategies and practical implementability. Here we present a simple, efficient and scalable framework aimed at achieving this, validated on an optoelectronic platform. This framework features our proposed training mechanism, state-skipping direct feedback alignment, together with our single-map architecture based on simple one-dimensional dynamics. The former eliminates the need to access the system's intermediate states during training, substantially simplifying error backpropagation, while the latter removes complex internal interconnections. Together, they enhance training efficiency and practical feasibility. Compared with conventional deep neural networks, our approach substantially reduces computational costs and trainable parameter count while achieving comparable performance. Integrated into modern architectures, our scheme attains improved performance with an approximately 40% reduction in computational resources. Notably, our approach challenges the scaling laws of conventional counterparts, underscoring its strong scalability and practical promise.