Deep learning at the edge enables real-time streaming ptychographic imaging

Babu, Anakha V.; Zhou, Tao; Kandel, Saugat; Bicer, Tekin; Liu, Zhengchun; Judge, William; Ching, Daniel J.; Jiang, Yi; Veseli, Sinisa; Henke, Steven; Chard, Ryan; Yao, Yudong; Sirazitdinova, Ekaterina; Gupta, Geetika; Holt, Martin V.; Foster, Ian T.; Miceli, Antonino; Cherukara, Mathew J.

doi:10.1038/s41467-023-41496-z

Title: Deep learning at the edge enables real-time streaming ptychographic imaging

Journal Article · Fri Nov 03 00:00:00 UTC 2023 · Nature Communications

DOI: https://doi.org/10.1038/s41467-023-41496-z · OSTI ID:2204731

Babu, Anakha V.; Zhou, Tao;

;

; Liu, Zhengchun; Judge, William;

;

; Chard, Ryan; Yao, Yudong; Sirazitdinova, Ekaterina; Gupta, Geetika;

;

Abstract Coherent imaging techniques provide an unparalleled multi-scale view of materials across scientific and technological fields, from structural materials to quantum devices, from integrated circuits to biological cells. Driven by the construction of brighter sources and high-rate detectors, coherent imaging methods like ptychography are poised to revolutionize nanoscale materials characterization. However, these advancements are accompanied by significant increase in data and compute needs, which precludes real-time imaging, feedback and decision-making capabilities with conventional approaches. Here, we demonstrate a workflow that leverages artificial intelligence at the edge and high-performance computing to enable real-time inversion on X-ray ptychography data streamed directly from a detector at up to 2 kHz. The proposed AI-enabled workflow eliminates the oversampling constraints, allowing low-dose imaging using orders of magnitude less data than required by traditional methods.

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Research Organization:: Argonne National Laboratory (ANL), Argonne, IL (United States). Advanced Photon Source (APS); Argonne National Laboratory (ANL), Argonne, IL (United States). Argonne Leadership Computing Facility (ALCF); Argonne National Laboratory (ANL), Argonne, IL (United States). Center for Nanoscale Materials (CNM)

Sponsoring Organization:: USDOE; USDOE Laboratory Directed Research and Development (LDRD) Program; USDOE Office of Science (SC), Basic Energy Sciences (BES). Scientific User Facilities (SUF)

Grant/Contract Number:: AC02-06CH11357

OSTI ID:: 2204731

Journal Information:: Nature Communications, Journal Name: Nature Communications Journal Issue: 1 Vol. 14; ISSN 2041-1723

Publisher:: Nature Publishing GroupCopyright Statement

Country of Publication:: United Kingdom

Language:: English

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