Abstract 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.