High-speed olfactory perception with adaptive load balancing based on a laser array reservoir computing architecture.

Abstract

In the front-end information acquisition module of intelligent olfactory systems, the inherent cross-sensitivity of gas sensors presents a significant technical challenge. While sensor-array-based architectures have been established as an effective solution to address this limitation, the requirements for real-time detection in gas identification and concentration quantification have introduced a new challenge: the intrinsic multi-channel information processing demands of array systems lead to a dramatic increase in computational complexity. In this work, we propose a photonic reservoir computing (RC) method for high-speed mixed gases olfactory perception, by leveraging the nonlinear mapping properties of semiconductor lasers and the inherent high-speed parallelism and low-energy characteristics of optical computing. A dimensional segmentation mechanism for multidimensional signals based on semiconductor laser arrays has been developed. By constructing a parallel PRC architecture, this mechanism enables distributed processing of multidimensional signals from gas sensor arrays, achieving adaptive matching between the number of activated lasers in the array and the internal feature dimensions required for computational load balancing. Numerical results indicate that the proposed system achieves high accuracy in gas classification tasks and concentration prediction performance comparable to current mainstream algorithms. This confirms the significant advantages of laser-array-based reservoirs in processing multivariable sensor data. The results provide a theoretical foundation for the development of physical RC systems oriented toward low-power rapid detection of mixed gases. With integration and miniaturization of photonic technologies, it is promising to build miniaturized brain-inspired computing systems with rapid inference capability and dynamic adaptability, thus contributing to the advancement of electronic nose technology.