Mass-Spring Models for Passive Keyword Spotting: A Springtronics Approach

2025-04-08

AI Summary

This work addresses keyword spotting in speech recognition by proposing “springtronics”—a purely passive mechanical computing paradigm that requires no electrical power or signal transduction. Methodologically, it introduces a hierarchical mechanical architecture based on nonlinear mass-spring systems, where low-order polynomial potential functions enable modular, customizable physical subsystems; physical dynamics-driven feature extraction is integrated with continuous-time convolution for end-to-end temporal signal processing. Crucially, this is the first demonstration of a passive mechanical system with hundreds of degrees of freedom applied to real-world speech benchmarks (e.g., Google Speech Commands). Experiments show classification accuracy comparable to sub-milliwatt electronic systems, validating the feasibility of high-dimensional nonlinear dynamical systems for complex temporal perception. The approach overcomes fundamental limitations of conventional reservoir computing—namely, physical realizability and scalability constraints imposed by predefined hardware topologies.

Technology Category

• Natural Language Processing: Speech
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Machine Learning: Hardware-aware ML

Application Category

• Search and Retrieval-Augmented AI: Assisted, interactive, and conversational search
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems

Abstract

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 primarily focused on elementary computations, implemented in systems based on pre-defined 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 considered mass-spring systems. 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 existing sub-mW electronic systems.

Problem

• Passive mechanical system for keyword spotting
• Hierarchical mass-spring model for speech signals
• Springtronics framework with low-power accuracy

Innovation

• Nonlinear mass-spring model for keyword spotting
• Hierarchical architecture with feature extraction
• Springtronics framework with polynomial potentials