Photonics for computing and computing for photonics
The liaison between photonics and computing is a pillar of modern optics and subject of cutting-edge research for more than half a century. As in many scientific disciplines, high-performance computational methods have become essential for describing, …
Primer on silicon neuromorphic photonic processors: architecture and compiler
Microelectronic computers have encountered challenges in meeting all of today’s demands for information processing. Meeting these demands will require the development of unconventional computers employing alternative processing models and new device …
Meta-optics for spatial optical analog computing
Rapidly growing demands for high-performance computing, powerful data processing, and big data necessitate the advent of novel optical devices to perform demanding computing processes effectively. Due to its unprecedented growth in the past two decades, the …
Integrated photonic FFT for photonic tensor operations towards efficient and high-speed neural networks
The technologically-relevant task of feature extraction from data performed in deep-learning systems is routinely accomplished as repeated fast Fourier transforms (FFT) electronically in prevalent domain-specific architectures such as in graphics processing …
Noise-enhanced spatial-photonic Ising machine
Ising machines are novel computing devices for the energy minimization of Ising models. These combinatorial optimization problems are of paramount importance for science and technology, but remain difficult to tackle on large scale by conventional …
Exact mapping between a laser network loss rate and the classical XY Hamiltonian by laser loss control
Recently, there has been growing interest in the utilization of physical systems as heuristic optimizers for classical spin Hamiltonians. A prominent approach employs gain-dissipative optical oscillator networks for this purpose. Unfortunately, these systems …
Polaritonic XY-Ising machine
Gain-dissipative systems of various physical origin have recently shown the ability to act as analogue minimisers of hard combinatorial optimisation problems. Whether or not these proposals will lead to any advantage in performance over the classical …
Boolean learning under noise-perturbations in hardware neural networks
A high efficiency hardware integration of neural networks benefits from realizing nonlinearity, network connectivity and learning fully in a physical substrate. Multiple systems have recently implemented some or all of these operations, yet the focus was …
NanoLEDs for energy-efficient and gigahertz-speed spike-based sub-? neuromorphic nanophotonic computing
Event-activated biological-inspired subwavelength (sub-?) photonic neural networks are of key importance for future energy-efficient and high-bandwidth artificial intelligence systems. However, a miniaturized light-emitting nanosource for spike-based …
Accelerating photonic computing by bandwidth enhancement of a time-delay reservoir
Semiconductor lasers (SLs) that are subject to delayed optical feedback and external optical injection have been demonstrated to perform information processing using the photonic reservoir computing paradigm. Optical injection or optical feedback can under …
Computer generated optical volume elements by additive manufacturing
Computer generated optical volume elements have been investigated for information storage, spectral filtering, and imaging applications. Advancements in additive manufacturing (3D printing) allow the fabrication of multilayered diffractive volume elements in …
Predictive and generative machine learning models for photonic crystals
The prediction and design of photonic features have traditionally been guided by theory-driven computational methods, spanning a wide range of direct solvers and optimization techniques. Motivated by enormous advances in the field of machine learning, there …
Nanolaser-based emulators of spin Hamiltonians
Finding the solution to a large category of optimization problems, known as the NP-hard class, requires an exponentially increasing solution time using conventional computers. Lately, there has been intense efforts to develop alternative computational methods …
Optical Potts machine through networks of three-photon down-conversion oscillators
In recent years, there has been a growing interest in optical simulation of lattice spin models for applications in unconventional computing. Here, we propose optical implementation of a three-state Potts spin model by using networks of coupled parametric …
Misalignment resilient diffractive optical networks
As an optical machine learning framework, Diffractive Deep Neural Networks (D2NN) take advantage of data-driven training methods used in deep learning to devise light–matter interaction in 3D for performing a desired statistical inference task. Multi-layer …
Opportunities for integrated photonic neural networks
Photonics offers exciting opportunities for neuromorphic computing. This paper specifically reviews the prospects of integrated optical solutions for accelerating inference and training of artificial neural networks. Calculating the synaptic function, …