Analogue computing with metamaterials
Despite their widespread use for performing advanced computational tasks, digital signal
processors suffer from several restrictions, including low speed, high power consumption …
processors suffer from several restrictions, including low speed, high power consumption …
Optical neural networks: progress and challenges
Artificial intelligence has prevailed in all trades and professions due to the assistance of big
data resources, advanced algorithms, and high-performance electronic hardware. However …
data resources, advanced algorithms, and high-performance electronic hardware. However …
Large-scale neuromorphic optoelectronic computing with a reconfigurable diffractive processing unit
There is an ever-growing demand for artificial intelligence. Optical processors, which
compute with photons instead of electrons, can fundamentally accelerate the development …
compute with photons instead of electrons, can fundamentally accelerate the development …
Photonic machine learning with on-chip diffractive optics
Abstract Machine learning technologies have been extensively applied in high-performance
information-processing fields. However, the computation rate of existing hardware is …
information-processing fields. However, the computation rate of existing hardware is …
Metasurface-enabled on-chip multiplexed diffractive neural networks in the visible
Replacing electrons with photons is a compelling route toward high-speed, massively
parallel, and low-power artificial intelligence computing. Recently, diffractive networks …
parallel, and low-power artificial intelligence computing. Recently, diffractive networks …
Fully forward mode training for optical neural networks
Optical computing promises to improve the speed and energy efficiency of machine learning
applications,,,,–. However, current approaches to efficiently train these models are limited by …
applications,,,,–. However, current approaches to efficiently train these models are limited by …
Direct retrieval of Zernike-based pupil functions using integrated diffractive deep neural networks
Retrieving the pupil phase of a beam path is a central problem for optical systems across
scales, from telescopes, where the phase information allows for aberration correction, to the …
scales, from telescopes, where the phase information allows for aberration correction, to the …
Photonic multiplexing techniques for neuromorphic computing
The simultaneous advances in artificial neural networks and photonic integration
technologies have spurred extensive research in optical computing and optical neural …
technologies have spurred extensive research in optical computing and optical neural …
Polarization multiplexed diffractive computing: all-optical implementation of a group of linear transformations through a polarization-encoded diffractive network
Research on optical computing has recently attracted significant attention due to the
transformative advances in machine learning. Among different approaches, diffractive …
transformative advances in machine learning. Among different approaches, diffractive …
Neuromorphic computing based on wavelength-division multiplexing
Optical neural networks (ONNs), or optical neuromorphic hardware accelerators, have the
potential to dramatically enhance the computing power and energy efficiency of mainstream …
potential to dramatically enhance the computing power and energy efficiency of mainstream …