Photonic (computational) memories: tunable nanophotonics for data storage and computing
The exponential growth of information stored in data centers and computational power required for various data-intensive applications, such as deep learning and AI, call for new strategies to improve or move beyond the traditional von Neumann architecture. Recent achievements in information storage...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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De Gruyter
2022-05-01
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| Series: | Nanophotonics |
| Subjects: | |
| Online Access: | https://doi.org/10.1515/nanoph-2022-0089 |
| _version_ | 1797789011631144960 |
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| author | Lian Chuanyu Vagionas Christos Alexoudi Theonitsa Pleros Nikos Youngblood Nathan Ríos Carlos |
| author_facet | Lian Chuanyu Vagionas Christos Alexoudi Theonitsa Pleros Nikos Youngblood Nathan Ríos Carlos |
| author_sort | Lian Chuanyu |
| collection | DOAJ |
| description | The exponential growth of information stored in data centers and computational power required for various data-intensive applications, such as deep learning and AI, call for new strategies to improve or move beyond the traditional von Neumann architecture. Recent achievements in information storage and computation in the optical domain, enabling energy-efficient, fast, and high-bandwidth data processing, show great potential for photonics to overcome the von Neumann bottleneck and reduce the energy wasted to Joule heating. Optically readable memories are fundamental in this process, and while light-based storage has traditionally (and commercially) employed free-space optics, recent developments in photonic integrated circuits (PICs) and optical nano-materials have opened the doors to new opportunities on-chip. Photonic memories have yet to rival their electronic digital counterparts in storage density; however, their inherent analog nature and ultrahigh bandwidth make them ideal for unconventional computing strategies. Here, we review emerging nanophotonic devices that possess memory capabilities by elaborating on their tunable mechanisms and evaluating them in terms of scalability and device performance. Moreover, we discuss the progress on large-scale architectures for photonic memory arrays and optical computing primarily based on memory performance. |
| first_indexed | 2024-03-13T01:44:44Z |
| format | Article |
| id | doaj.art-72d610e640f74545bf1204cf4cc8878d |
| institution | Directory Open Access Journal |
| issn | 2192-8614 |
| language | English |
| last_indexed | 2024-03-13T01:44:44Z |
| publishDate | 2022-05-01 |
| publisher | De Gruyter |
| record_format | Article |
| series | Nanophotonics |
| spelling | doaj.art-72d610e640f74545bf1204cf4cc8878d2023-07-03T10:20:07ZengDe GruyterNanophotonics2192-86142022-05-0111173823385410.1515/nanoph-2022-0089Photonic (computational) memories: tunable nanophotonics for data storage and computingLian Chuanyu0Vagionas Christos1Alexoudi Theonitsa2Pleros Nikos3Youngblood Nathan4Ríos Carlos5Department of Materials Science & Engineering, University of Maryland, College Park, MD, USADepartment of Informatics, Aristotle University of Thessaloniki, Thessaloniki, GreeceDepartment of Informatics, Aristotle University of Thessaloniki, Thessaloniki, GreeceDepartment of Informatics, Aristotle University of Thessaloniki, Thessaloniki, GreeceDepartment of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA, USADepartment of Materials Science & Engineering, University of Maryland, College Park, MD, USAThe exponential growth of information stored in data centers and computational power required for various data-intensive applications, such as deep learning and AI, call for new strategies to improve or move beyond the traditional von Neumann architecture. Recent achievements in information storage and computation in the optical domain, enabling energy-efficient, fast, and high-bandwidth data processing, show great potential for photonics to overcome the von Neumann bottleneck and reduce the energy wasted to Joule heating. Optically readable memories are fundamental in this process, and while light-based storage has traditionally (and commercially) employed free-space optics, recent developments in photonic integrated circuits (PICs) and optical nano-materials have opened the doors to new opportunities on-chip. Photonic memories have yet to rival their electronic digital counterparts in storage density; however, their inherent analog nature and ultrahigh bandwidth make them ideal for unconventional computing strategies. Here, we review emerging nanophotonic devices that possess memory capabilities by elaborating on their tunable mechanisms and evaluating them in terms of scalability and device performance. Moreover, we discuss the progress on large-scale architectures for photonic memory arrays and optical computing primarily based on memory performance.https://doi.org/10.1515/nanoph-2022-0089optoelectronicsphotonic computingphotonic memorytunable nanophotonics |
| spellingShingle | Lian Chuanyu Vagionas Christos Alexoudi Theonitsa Pleros Nikos Youngblood Nathan Ríos Carlos Photonic (computational) memories: tunable nanophotonics for data storage and computing Nanophotonics optoelectronics photonic computing photonic memory tunable nanophotonics |
| title | Photonic (computational) memories: tunable nanophotonics for data storage and computing |
| title_full | Photonic (computational) memories: tunable nanophotonics for data storage and computing |
| title_fullStr | Photonic (computational) memories: tunable nanophotonics for data storage and computing |
| title_full_unstemmed | Photonic (computational) memories: tunable nanophotonics for data storage and computing |
| title_short | Photonic (computational) memories: tunable nanophotonics for data storage and computing |
| title_sort | photonic computational memories tunable nanophotonics for data storage and computing |
| topic | optoelectronics photonic computing photonic memory tunable nanophotonics |
| url | https://doi.org/10.1515/nanoph-2022-0089 |
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