Fully Analog Resonant Recurrent Neural Network via Metacircuit

TL;DR

This paper introduces a fully analog resonant recurrent neural network (R$^2$NN) built with a metacircuit architecture, enabling real-time temporal classification directly from analog inputs with high efficiency.

Contribution

It presents a novel metacircuit-based implementation of a fully analog RNN that accurately maps trained models onto physical hardware for edge intelligence.

Findings

Successfully demonstrated real-time classification in tactile, speech, and monitoring tasks.

Achieved direct spectral feature extraction without analog-to-digital conversion.

Established a scalable, low-latency analog neural hardware platform.

Abstract

Physical neural networks offer a transformative route to edge intelligence, providing superior inference speed and energy efficiency compared to conventional digital architectures. However, realizing scalable, end-to-end, fully analog recurrent neural networks for temporal information processing remains challenging due to the difficulty of faithfully mapping trained network models onto physical hardware. Here we present a fully analog resonant recurrent neural network (R$^2$NN) implemented via a metacircuit architecture composed of coupled electrical local resonators. A reformulated mechanical-electrical analogy establishes a direct mapping between the R$^2$NN model and metacircuit elements, enabling accurate physical implementation of trained neural network parameters. By integrating jointly trainable global resistive coupling and local resonances, which generate effective frequency-dependent negative resistances, the architecture shapes an impedance landscape that steers currents along frequency-selective pathways. This mechanism enables direct extraction of discriminative spectral features, facilitating real-time temporal classification of raw analog inputs while bypassing analog-to-digital conversion. We demonstrate the cross-domain versatility of this framework using integrated hardware for tactile perception, speech recognition, and condition monitoring. This work establishes a scalable, fully analog paradigm for intelligent temporal processing and paves the way for low-latency, resource-efficient physical neural hardware for edge intelligence.