MicroLEDs for optical neuromorphic computing—application potential and present challenges
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| Název: | MicroLEDs for optical neuromorphic computing—application potential and present challenges |
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| Autoři: | R Kraneis, M Müller, S Higgins-Wood, N Kaelin, H-N Wehmann, N von Malm, C Werner, A Waag |
| Zdroj: | JPhys Photonics, Vol 6, Iss 4, p 04LT01 (2024) |
| Informace o vydavateli: | IOP Publishing, 2024. |
| Rok vydání: | 2024 |
| Sbírka: | LCC:Applied optics. Photonics LCC:Optics. Light |
| Témata: | neuromorphic computing, optical neural network, microLED, gallium nitride, Applied optics. Photonics, TA1501-1820, Optics. Light, QC350-467 |
| Popis: | The slow non-radiative surface recombination velocity of gallium nitride (GaN) in combination with its highly efficient radiative recombination makes this material ideally suited for microLEDs with dimensions as small as 1 µ m and even below, serving as the fundamental building block of micro-displays. However, due to their superior miniaturization potential and energy efficiency, GaN-based microLEDs have applications that extend well beyond display technology. Their capability to produce optical patterns with high resolution, which can be modulated at extremely high frequencies, makes them suitable for numerous other applications. We suggest exploiting these exciting properties for a new and potentially equally significant application: utilizing microLEDs in optical processing units for artificial intelligence workloads. In neuromorphic computing, relevant aspects of biological neural networks are emulated directly with either electronic circuits or photonic devices, avoiding the shortcomings of conventional digital computer technology for AI workloads, which generally require massively parallel information processing. GaN microLEDs are discussed here as a promising enabling technology for optical neuromorphic processing units. We see great potential to substantially decrease power consumption through massively parallel in-memory processing combined with efficient photon production and detection. A theoretical analysis of scalability and energy efficiency is provided. A macroscopic bench-top optical microLED demonstrator is presented, which experimentally proves the feasibility of our approach. Future potential and challenges associated with miniaturizing and scaling microLED-based optical processing units are discussed. Finally, we summarize the open research questions that require attention before fully functional and miniaturized optical neuromorphic processing units based on GaN microLEDs can be realized. |
| Druh dokumentu: | article |
| Popis souboru: | electronic resource |
| Jazyk: | English |
| ISSN: | 2515-7647 |
| Relation: | https://doaj.org/toc/2515-7647 |
| DOI: | 10.1088/2515-7647/ad8615 |
| Přístupová URL adresa: | https://doaj.org/article/b52037bd9c1742e39facc83da30f77e4 |
| Přístupové číslo: | edsdoj.b52037bd9c1742e39facc83da30f77e4 |
| Databáze: | Directory of Open Access Journals |
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Nájsť tento článok vo Web of Science | Abstrakt: | The slow non-radiative surface recombination velocity of gallium nitride (GaN) in combination with its highly efficient radiative recombination makes this material ideally suited for microLEDs with dimensions as small as 1 µ m and even below, serving as the fundamental building block of micro-displays. However, due to their superior miniaturization potential and energy efficiency, GaN-based microLEDs have applications that extend well beyond display technology. Their capability to produce optical patterns with high resolution, which can be modulated at extremely high frequencies, makes them suitable for numerous other applications. We suggest exploiting these exciting properties for a new and potentially equally significant application: utilizing microLEDs in optical processing units for artificial intelligence workloads. In neuromorphic computing, relevant aspects of biological neural networks are emulated directly with either electronic circuits or photonic devices, avoiding the shortcomings of conventional digital computer technology for AI workloads, which generally require massively parallel information processing. GaN microLEDs are discussed here as a promising enabling technology for optical neuromorphic processing units. We see great potential to substantially decrease power consumption through massively parallel in-memory processing combined with efficient photon production and detection. A theoretical analysis of scalability and energy efficiency is provided. A macroscopic bench-top optical microLED demonstrator is presented, which experimentally proves the feasibility of our approach. Future potential and challenges associated with miniaturizing and scaling microLED-based optical processing units are discussed. Finally, we summarize the open research questions that require attention before fully functional and miniaturized optical neuromorphic processing units based on GaN microLEDs can be realized. |
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| ISSN: | 25157647 |
| DOI: | 10.1088/2515-7647/ad8615 |