Abstract
AbstractMatrix computation, as a fundamental building block of information processing in science and technology, contributes most of the computational overheads in modern signal processing and artificial intelligence algorithms. Photonic accelerators are designed to accelerate specific categories of computing in the optical domain, especially matrix multiplication, to address the growing demand for computing resources and capacity. Photonic matrix multiplication has much potential to expand the domain of telecommunication, and artificial intelligence benefiting from its superior performance. Recent research in photonic matrix multiplication has flourished and may provide opportunities to develop applications that are unachievable at present by conventional electronic processors. In this review, we first introduce the methods of photonic matrix multiplication, mainly including the plane light conversion method, Mach–Zehnder interferometer method and wavelength division multiplexing method. We also summarize the developmental milestones of photonic matrix multiplication and the related applications. Then, we review their detailed advances in applications to optical signal processing and artificial neural networks in recent years. Finally, we comment on the challenges and perspectives of photonic matrix multiplication and photonic acceleration.
Publisher
Springer Science and Business Media LLC
Subject
Atomic and Molecular Physics, and Optics,Electronic, Optical and Magnetic Materials
Reference175 articles.
1. Kitayama, K.-I. et al. Novel frontier of photonics for data processing—Photonic accelerator. APL Photonics 4, 090901 (2019).
2. Waldrop, M. M. The chips are down for Moore’s law. Nature 530, 144–147 (2016).
3. Shen, Y. C. et al. Deep learning with coherent nanophotonic circuits. Nat. Photonics 11, 441–446 (2017).
4. Lee, D. D. & Seung, H. S. Learning the parts of objects by non-negative matrix factorization. Nature 401, 788–791 (1999).
5. Richardson, M. & Domingos, P. The intelligent surfer: probabilistic combination of link and content information in pagerank. In Proceedings of the 14th International Conference on Neural Information Processing Systems: Natural and Synthetic. 1441–1448 (Vancouver British, Columbia, Canada: MIT Press, 2001).
Cited by
150 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献