Parallel convolutional processing using an integrated photonic tensor core
Impact in
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- Journal
- Oxford University Research Archive (ORA) (University of Oxford)
In The Last Decade
doi.org/w16169848 →Countries where authors are citing Parallel convolutional processing using an integrated photonic tensor core
This map shows the geographic impact of Parallel convolutional processing using an integrated photonic tensor core. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Parallel convolutional processing using an integrated photonic tensor core with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Parallel convolutional processing using an integrated photonic tensor core more than expected).
Fields of papers citing Parallel convolutional processing using an integrated photonic tensor core
This network shows the impact of Parallel convolutional processing using an integrated photonic tensor core. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Parallel convolutional processing using an integrated photonic tensor core.
About Parallel convolutional processing using an integrated photonic tensor core
This paper, published in 2021, received 971 indexed citations . Written by Anton Lukashchuk, Manuel Le Gallo, Abu Sebastian, Junqiu Liu, Xin Fu, Harish Bhaskaran, Nathan Youngblood, Johannes Feldmann, Helge Gehring and Maxim Karpov covering the research area of Artificial Intelligence and Electrical and Electronic Engineering. It is primarily cited by scholars working on Electrical and Electronic Engineering (904 citations), Artificial Intelligence (714 citations), Atomic and Molecular Physics, and Optics (201 citations), Materials Chemistry (119 citations) and Biomedical Engineering (42 citations). Published in Oxford University Research Archive (ORA) (University of Oxford).
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.
This paper is also available at doi.org/w16169848.