Harnessing Phase Dynamics Across Diverse Frequencies with Multifrequency Oscillatory Neural Networks

Oscillatory Neural Networks (ONNs) are an emerging computing paradigm that encodes information in the phase difference of coupled oscillators. Traditionally, ONNs have been investigated using homogeneous frequency oscillators. However, physical hardware implementations are inherently subject to frequency mismatches, device variability, and nonuniformities, which are typically perceived as undesirable for circuit performance. This work challenges this assumption by inquiring whether frequency discrepancies and multifrequency oscillators can be harnessed for performing computation. First, the frequency variation threshold that oscillators can withstand to compute is derived. Next, for frequency variations beyond such a threshold, a multifrequency ONN computing approach is introduced and validated in both hardware implementation and software simulations by performing pattern retrieval. The results demonstrate computation with nonuniform frequency oscillators and highlight the feasibility of their physical hardware implementation.