Physical Computing with Coupled Oscillators – Current Progress and Future Directions

Dynamical systems exhibit rich and intricate behaviors that can be exploited for physical computation. Physical computing draws inspiration from complex systems that continuously adapt, self-organize, and minimize energy to reach stable configurations, inherently enabling parallel processing. These features show promise in solving intractable scientific problems, such as NP-hard combinatorial optimization problems. Yet, engineering such dynamical systems for computing remains a challenge in terms of technology choice and scalable circuit design implementation. This paper presents an overview of recent advances and future directions in physical computing using coupled oscillatory neural networks (ONNs).