Resonance for Analog Recurrent Neural Network
Yurui Qu, Ming Xing Zhou, Erfan Khoram, Nanfang Yu, Zongfu Yu · ACS Photonics · 2022
There is a strong interest in using physical waves for artificial neural computing because of their unique advantages in fast speed and intrinsic parallelism. Resonance, as a ubiquitous feature across many wave systems, is a natural candidate for analog computing in temporal signals. We demonstrate that resonance can be used to construct stable and scalable recurrent neural networks. By including resonators with different lifetimes, the computing system develops both short-term and long-term memories simultaneously.