Synchronization Detection in Networks of Coupled Oscillators for Pattern Recognition

TL;DR

This paper evaluates two simple, CMOS-compatible methods for detecting synchronization patterns in coupled oscillator networks, demonstrating their effectiveness and robustness, which facilitates hardware implementation of oscillator-based neural systems.

Contribution

It introduces and compares two practical synchronization readout schemes for coupled oscillator networks suitable for hardware neural networks, showing they match complex statistical methods in accuracy and improve noise resilience.

Findings

Readout schemes are nearly as accurate as full statistical evaluation.

Proposed methods show better noise resilience than previous approaches.

Results enable practical hardware realization of oscillator-based neural networks.

Abstract

Coupled oscillator-based networks are an attractive approach for implementing hardware neural networks based on emerging nanotechnologies. However, the readout of the state of a coupled oscillator network is a difficult challenge in hardware implementations, as it necessitates complex signal processing to evaluate the degree of synchronization between oscillators, possibly more complicated than the coupled oscillator network itself. In this work, we focus on a coupled oscillator network particularly adapted to emerging technologies, and evaluate two schemes for reading synchronization patterns that can be readily implemented with basic CMOS circuits. Through simulation of a simple generic coupled oscillator network, we compare the operation of these readout techniques with a previously proposed full statistics evaluation scheme. Our approaches provide results nearly identical to the mathematical method, but also show better resilience to moderate noise, which is a major concern for hardware implementations. These results open the door to widespread realization of hardware coupled oscillator-based neural systems.