Large-scale photonic natural language processing
| Autor: | Valensise, Carlo Michele, Grecco, Ivana, Pierangeli, Davide, Conti, Claudio |
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| Rok vydání: | 2022 |
| Předmět: | |
| Druh dokumentu: | Working Paper |
| Popis: | Modern machine learning applications require huge artificial networks demanding in computational power and memory. Light-based platforms promise ultra-fast and energy-efficient hardware, which may help in realizing next-generation data processing devices. However, current photonic networks are limited by the number of input-output nodes that can be processed in a single shot. This restricted network capacity prevents their application to relevant large-scale problems such as natural language processing. Here, we realize a photonic processor with a capacity exceeding $1.5 \times 10^{10}$ optical nodes, more than one order of magnitude larger than any previous implementation, which enables photonic large-scale text encoding and classification. By exploiting the full three-dimensional structure of the optical field propagating in free space, we overcome the interpolation threshold and reach the over-parametrized region of machine learning, a condition that allows high-performance natural language processing with a minimal fraction of training points. Our results provide a novel solution to scale-up light-driven computing and open the route to photonic language processing. Comment: 12 pages, 4 figures, 1 table |
| Databáze: | arXiv |
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