Photonic Compute-in-Wire: Remotely Driven Photonic Deep Neural Network with a Single Nonlinear Loop

The development of deep learning has motivated the advancement of unconventional computing that leverages analog physical systems such as analog electronics, spintronics, and photonics. These technologies have also led to the development of unique computational paradigms harnessing the features of analog devices, including compute-in-memory for nonvolatile devices and compute-in-sensor for analog electronics. What, then, are the computational paradigms that can exploit the characteristics of photonics? Optical computing has emerged as a promising candidate as it offers low-latency and low-power computation by utilizing the inherent parallelism of light. Additionally, the low-loss medium of optical fibers allows for the transmission of information over long distances. In this paper, we experimentally demonstrate a remotely driven optical neural network that combines these advantageous features. Namely, computations can be executed with data transfer over a photonic network, which provides a computational paradigm named photonic compute-in-wire. As a demonstration, we constructed an optoelectronic benchtop with a 20-km fiber access line. To facilitate the implementation of this concept, we extended the folded-in-time deep neural network (FiT-DNN) approach, which utilizes a single delay line with a nonlinear device, a dispersive element, and a modulator. This allows the insertion of a photonic DNN within a single transmission line. The parameters of our constructed photonic FiT-DNN were trained by using a digital twin of nonlinear optoelectronic dynamics. Through our benchmark experiments, we confirmed good accuracy for image recognition tasks. Our approach broadens the opportunities to utilize optical computation from local edge computing to in-network computing for low-latency and low-energy computation.