On-chip bacterial foraging training in silicon photonic circuits for projection-enabled nonlinear classification.

ABSTRACT: On-chip training remains a challenging issue for photonic devices to implement machine learning algorithms. Most demonstrations only implement inference in photonics for offline-trained neural network models. On the other hand, artificial neural networks are one of the most deployed algorithms, while other machine learning algorithms such as supporting vector machine (SVM) remain unexplored in photonics. Here, inspired by SVM, we propose to implement projection-based classification principle by constructing nonlinear mapping functions in silicon photonic circuits and experimentally demonstrate on-chip bacterial foraging training for this principle to realize single Boolean logics, combinational Boolean logics, and Iris classification with ~96.7 - 98.3 per cent accuracy. This approach can o

SUBMITTER: Cong G 

PROVIDER: S-EPMC9247170 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature