Reconfigurable versatile integrated photonic computing chip
Abstract With the rapid development of information technology, artificial intelligence and large-scale models have exhibited exceptional performance and widespread applications. Photonic hardware offers a promising solution to meet the growing demands for computational power and energy efficiency. R...
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| Main Authors: | , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2025-08-01
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| Series: | eLight |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s43593-025-00098-6 |
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| Summary: | Abstract With the rapid development of information technology, artificial intelligence and large-scale models have exhibited exceptional performance and widespread applications. Photonic hardware offers a promising solution to meet the growing demands for computational power and energy efficiency. Researchers have aimed to develop an efficient integrated photonic computing chip capable of supporting a wide range of application scenarios in both static and dynamic temporal domains. However, with several mainstream photonic components already well-developed, achieving fundamental breakthroughs at the level of basic computing units remains highly challenging. Here, we report a novel algorithm-hardware co-design strategy that enables in situ reconfigurability across diverse neural network models, all within a unified photonic configuration. We unlock the intrinsic capabilities of a compact cross-waveguide coupled microring component to natively support both static and dynamic temporal tasks. As a proof of concept, we experimentally integrated a turnkey soliton microcomb as the light source on the photonic computing platform, demonstrating the realization of fully connected, convolutional, and recurrent neural network models within a unified structure. The chip achieves area computing efficiency of up to 2.45 TOPS/mm2 for 208 tunable components. We evaluate the performance of the proposed chip by implementing image classification tasks on the MNIST and CIFAR-10 datasets, achieving measured test accuracies of 92.93% and 56.57%, respectively. Sentiment analysis on the IMDB dataset achieves a measured test accuracy of 80.81%. Furthermore, speech recognition is implemented by combining three neural networks within a scaled-up architecture. This work addresses the challenges of performing versatile computations on integrated photonic platforms, offering a promising solution for chip-integrated multifunctional photonic information processing. |
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| ISSN: | 2097-1710 2662-8643 |