Time-wavelength multiplexed photonic neural network accelerator for distributed acoustic sensing systems

Abstract

Distributed acoustic sensors (DASs) can effectively monitor acoustic fields along sensing fibers with high sensitivity and high response speed. However, their data processing is limited by the performance of electronic signal processing, hindering real-time applications. The time-wavelength multiplexed photonic neural network accelerator (TWM-PNNA), which uses photons instead of electrons for operations, significantly enhances processing speed and energy efficiency. Therefore, we explore the feasibility of applying TWM-PNNA to DAS systems. We first discuss processing large DAS system data for compatibility with the TWM-PNNA system. We also investigate the effects of chirp on optical convolution in complex tasks and methods to mitigate its impact on classification accuracy. Furthermore, we propose a method for achieving an optical full connection and study the influence of pruning on the full connection to reduce the computational burden of the model. Experimental results indicate that decreasing the ratio of Δλchirp∕Δλ or choosing push–pull modulation can eliminate the impact of chirp on recognition accuracy. In addition, when the full connection parameter retention rate is no less than 60%, it can still maintain a classification accuracy of over 90%. TWM-PNNA provides an innovative computational framework for DAS systems, paving the way for the all-optical fusion of DAS systems with computational systems.