Photonic Bayesian Neural Network Using Programmed Optical Noises

Abstract

The Bayesian neural network (BNN) combines the strengths of neural networks and statistical modeling in that it simultaneously performs posterior predictions and quantifies the uncertainty of the predictions. Integrated photonics has emerged as a promising hardware platform of neural network accelerators capable of energy-efficient, low latency, and parallel computing. However, photonic neural networks demonstrated to date are mostly deterministic network models. Here, we extend the photonic neural network to a statistical model and proposed a photonic Bayesian neural network (P-BNN) architecture based on the integrated photonic platform and harnessing the inherent optical noises. The Bayesian neuron is realized by controlling the probability distribution of the signal-amplified spontaneous emission (signal-ASE) beat noise. We show the P-BNN's advantages in making predictions using the posterior distribution by simulating a p-BNN to perform handwritten number classification tasks. The simulation results show that the proposed P-BNN not only makes successful predictions on the expected images from the test dataset but also detects and rejects the unexpected images outside the training datasets. The P-BNN architecture is compatible with on-chip optical amplifiers and can be scaled up using current and emerging integrated photonics technologies, thus is promising for practical neural network applications.