General framework for signal processing in nonlinear mass-spring networks with application to keyword spotting

Mechanical systems played a foundational role in computing history, and have regained interest due to their unique properties, such as low damping and the ability to process mechanical signals without transduction. However, recent efforts have focused primarily on elementary computations, implemented in systems based on predefined reservoirs, or in periodic systems such as arrays of buckling beams. Here we numerically demonstrate a passive mechanical system—in the form of a nonlinear mass-spring model—that tackles a real-world benchmark for keyword spotting in speech signals. The model is organized in a hierarchical architecture combining feature extraction and continuous-time convolution, with each individual stage tailored to the physics of the mass-spring systems considered. For each step in the computation, a subsystem is designed by combining a small set of low-order polynomial potentials. These potentials act as fundamental components that interconnect a network of masses. In analogy to electronic circuit design, where complex functional circuits are constructed by combining basic components into hierarchical designs, we refer to this framework as “springtronics.” We introduce springtronic systems with hundreds of degrees of freedom, achieving speech classification accuracy comparable to that of existing submilliwatt electronic systems.