Partial coherence enhances parallelized photonic computing.

Journal: Nature
PMID: 39085539

Abstract

Advancements in optical coherence control have unlocked many cutting-edge applications, including long-haul communication, light detection and ranging (LiDAR) and optical coherence tomography. Prevailing wisdom suggests that using more coherent light sources leads to enhanced system performance and device functionalities. Our study introduces a photonic convolutional processing system that takes advantage of partially coherent light to boost computing parallelism without substantially sacrificing accuracy, potentially enabling larger-size photonic tensor cores. The reduction of the degree of coherence optimizes bandwidth use in the photonic convolutional processing system. This breakthrough challenges the traditional belief that coherence is essential or even advantageous in integrated photonic accelerators, thereby enabling the use of light sources with less rigorous feedback control and thermal-management requirements for high-throughput photonic computing. Here we demonstrate such a system in two photonic platforms for computing applications: a photonic tensor core using phase-change-material photonic memories that delivers parallel convolution operations to classify the gaits of ten patients with Parkinson's disease with 92.2% accuracy (92.7% theoretically) and a silicon photonic tensor core with embedded electro-absorption modulators (EAMs) to facilitate 0.108 tera operations per second (TOPS) convolutional processing for classifying the Modified National Institute of Standards and Technology (MNIST) handwritten digits dataset with 92.4% accuracy (95.0% theoretically).

Authors

  • Bowei Dong
    Department of Materials, University of Oxford, Oxford, UK.
  • Frank Brückerhoff-Plückelmann
    Kirchhoff-Institute for Physics, Heidelberg University, Heidelberg, Germany.
  • Lennart Meyer
    Kirchhoff-Institute for Physics, Heidelberg University, Heidelberg, Germany.
  • Jelle Dijkstra
    Kirchhoff-Institute for Physics, Heidelberg University, Heidelberg, Germany.
  • Ivonne Bente
    Center for NanoTechnology, University of Münster, Münster, Germany.
  • Daniel Wendland
    Center for NanoTechnology, University of Münster, Münster, Germany.
  • Akhil Varri
    Center for NanoTechnology, University of Münster, Münster, Germany.
  • Samarth Aggarwal
    Department of Materials, University of Oxford, Parks Road, Oxford OX1 3PH, U.K.
  • Nikolaos Farmakidis
    Department of Materials, University of Oxford, Parks Road, Oxford, OX1 3PH, UK.
  • Mengyun Wang
    Department of Materials, University of Oxford, Parks Road, Oxford, OX1 3PH, UK.
  • Guoce Yang
    Department of Materials, University of Oxford, Parks Road, Oxford, OX1 3PH, UK.
  • June Sang Lee
    Department of Materials, University of Oxford, Parks Road, Oxford, OX1 3PH, UK.
  • Yuhan He
    School of Mental Health, Wenzhou Medical University, Wenzhou, 325035, China.
  • Emmanuel Gooskens
    Photonics Research Group, Ghent University - imec, Ghent, Belgium.
  • Dim-Lee Kwong
    Institute of Microelectronics, Agency for Science, Technology and Research (A*STAR), Singapore, Singapore.
  • Peter Bienstman
  • Wolfram H P Pernice
    Kirchhoff-Institute for Physics, Heidelberg University, Heidelberg, Germany.
  • Harish Bhaskaran
    Department of Materials, University of Oxford, Parks Road, Oxford, OX1 3PH, UK. harish.bhaskaran@materials.ox.ac.uk.

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