Details
Original language | English |
---|---|
Number of pages | 13 |
Publication status | E-pub ahead of print - 11 Jun 2024 |
Externally published | Yes |
Abstract
Sustainable Development Goals
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2024.
Research output: Working paper/Preprint › Preprint
}
TY - UNPB
T1 - MicroLEDs for optical neuromorphic computing
T2 - Application potential and present challenges
AU - Kraneis, Robert
AU - Müller, Maximilian
AU - Higgins-Wood, Steffen
AU - Kälin, Noah
AU - Wehmann, Hergo Heinrich
AU - von Malm, Norwin
AU - Werner, Christian
AU - Waag, Andreas
PY - 2024/6/11
Y1 - 2024/6/11
N2 - 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 μm 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 will demonstrate their 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
AB - 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 μm 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 will demonstrate their 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
U2 - 10.24355/dbbs.084-202406111128-0
DO - 10.24355/dbbs.084-202406111128-0
M3 - Preprint
BT - MicroLEDs for optical neuromorphic computing
ER -