Computing with Coupled Oscillator Networks: Optimization, Memory, and Modern AI Primitives

We explore how synchronization in coupled oscillator networks may be employed to perform a variety of computing tasks. These systems are becoming more attractive as an alternative to conventional digital computers due to the increasing energy demands of the latter, particularly for large-scale problems in machine learning and artificial intelligence. The ubiquity of coupled oscillator networks means that oscillator-based computing can be thought of as a universal computing paradigm encompassing systems with a diversity of physical realizations, e.g. as analog circuits, neurological systems, spintronic systems. The bridge between the dynamics of coupled oscillator networks and computational primitives they implement is the Ising model, which originates in statistical physics, and when applied to oscillator networks, explains the emergence of globally complex and disordered synchronization patterns from local interactions in the network. We discuss the application of coupled oscillator networks to three fundamental computing operations. First, oscillator networks as specialized hardware solvers of NP-hard combinatorial optimization problems, with the solution encoded in the synchrony patterns. Second, oscillator networks as associative memory systems, enabling storage and retrieval of data represented through phases of oscillations. Finally, oscillator networks as a dynamical implementation of attention, with synchronization providing the mechanism for the alignment of keys to queries. The ability of oscillator networks to implement these computational primitives paves the way for novel realizations of intelligent systems through a variety of physical substrates.