Partial coherence enhances parallelized photonic computing

Bowei Dong, Frank Brückerhoff-Plückelmann, Lennart Meyer, Jelle Dijkstra, Ivonne Bente, Daniel Wendland, Akhil Varri, Samarth Aggarwal, Nikolaos Farmakidis, Mengyun Wang, Guoce Yang, June Sang Lee, Yuhan He, Emmanuel Gooskens, Dim-Lee Kwong, Peter Bienstman, Wolfram H. P. Pernice and Harish Bhaskaran ()
Additional contact information
Bowei Dong: University of Oxford
Frank Brückerhoff-Plückelmann: Heidelberg University
Lennart Meyer: Heidelberg University
Jelle Dijkstra: Heidelberg University
Ivonne Bente: University of Münster
Daniel Wendland: University of Münster
Akhil Varri: University of Münster
Samarth Aggarwal: University of Oxford
Nikolaos Farmakidis: University of Oxford
Mengyun Wang: University of Oxford
Guoce Yang: University of Oxford
June Sang Lee: University of Oxford
Yuhan He: University of Oxford
Emmanuel Gooskens: Ghent University – imec
Dim-Lee Kwong: Agency for Science, Technology and Research (A*STAR)
Peter Bienstman: Ghent University – imec
Wolfram H. P. Pernice: Heidelberg University
Harish Bhaskaran: University of Oxford

Nature, 2024, vol. 632, issue 8023, 55-62

Abstract: Abstract Advancements in optical coherence control1–5 have unlocked many cutting-edge applications, including long-haul communication, light detection and ranging (LiDAR) and optical coherence tomography6–8. Prevailing wisdom suggests that using more coherent light sources leads to enhanced system performance and device functionalities9–11. 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).

Date: 2024
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DOI: 10.1038/s41586-024-07590-y

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