Radio-frequency photonic architecture for deep neural networks, signal processing, and computing

Abstract

A multiplicative analog frequency transform optical neural network (MAFT-ONN) encodes data in the frequency domain, achieves matrix-vector products in a single shot using photoelectric multiplication, and uses a single electro-optic modulator for the nonlinear activation of all neurons in each layer. Photoelectric multiplication between radio frequency (RF)-encoded optical frequency combs allows single-shot matrix-vector multiplication and nonlinear activation, leading to high throughput and ultra-low latency. This frequency-encoding scheme can be implemented with several neurons per hardware spatial mode and allows for an arbitrary number of layers to be cascaded in the analog domain. For example, a three-layer DNN can compute over four million fully analog operations and implement both a convolutional and fully connected layer. Additionally, a MAFT-ONN can perform analog DNN inference of temporal waveforms like voice or radio signals, achieving bandwidth-limited throughput, speed of light-limited latency, and fully analog complex-valued matrix operations.