Analog Photonics Computing for Information Processing, Inference and Optimisation

• URL: http://arxiv.org/abs/2301.11760v2
• Date: Mon, 5 Jun 2023 13:00:52 GMT
• Title: Analog Photonics Computing for Information Processing, Inference and Optimisation
• Authors: Nikita Stroev and Natalia G. Berloff
• Abstract summary: Review presents an overview of the current state-of-the-art in photonics computing. Uses photons, photons coupled with matter, and optics-related technologies for effective and efficient computational purposes.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This review presents an overview of the current state-of-the-art in photonics computing, which leverages photons, photons coupled with matter, and optics-related technologies for effective and efficient computational purposes. It covers the history and development of photonics computing and modern analogue computing platforms and architectures, focusing on optimization tasks and neural network implementations. The authors examine special-purpose optimizers, mathematical descriptions of photonics optimizers, and their various interconnections. Disparate applications are discussed, including direct encoding, logistics, finance, phase retrieval, machine learning, neural networks, probabilistic graphical models, and image processing, among many others. The main directions of technological advancement and associated challenges in photonics computing are explored, along with an assessment of its efficiency. Finally, the paper discusses prospects and the field of optical quantum computing, providing insights into the potential applications of this technology.

Related papers

• Information processing at the speed of light [0.0]
  The introduction of quantum photonic chips has ushered in an era marked by scalability, stability, and cost-effectiveness. This article provides a comprehensive exploration of photonic quantum computing, covering key aspects such as encoding information in photons. The review further navigates the path towards establishing scalable and fault-tolerant photonic quantum computers.
  arXiv  Detail & Related papers  (2024-10-01T06:43:44Z)
• Artificial Neural Networks for Photonic Applications: From Algorithms to Implementation [1.7371307928431834]
  This tutorial-review on applications of artificial neural networks in photonics targets a broad audience. We focus here on the research areas at the interface between these disciplines, attempting to find the right balance between technical details specific to each domain and overall clarity.
  arXiv  Detail & Related papers  (2024-08-02T08:22:49Z)
• Optical Computing for Deep Neural Network Acceleration: Foundations, Recent Developments, and Emerging Directions [3.943289808718775]
  We discuss the fundamentals and state-of-the-art developments in optical computing, with an emphasis on deep neural networks (DNNs) Various promising approaches are described for engineering optical devices, enhancing optical circuits, and designing architectures that can adapt optical computing to a variety of DNN workloads. Novel techniques for hardware/software co-design that can intelligently tune and map DNN models to improve performance and energy-efficiency on optical computing platforms across high performance and resource constrained embedded, edge, and IoT platforms are also discussed.
  arXiv  Detail & Related papers  (2024-07-30T20:50:30Z)
• Mechanistic Neural Networks for Scientific Machine Learning [58.99592521721158]
  We present Mechanistic Neural Networks, a neural network design for machine learning applications in the sciences. It incorporates a new Mechanistic Block in standard architectures to explicitly learn governing differential equations as representations. Central to our approach is a novel Relaxed Linear Programming solver (NeuRLP) inspired by a technique that reduces solving linear ODEs to solving linear programs.
  arXiv  Detail & Related papers  (2024-02-20T15:23:24Z)
• Computation-efficient Deep Learning for Computer Vision: A Survey [121.84121397440337]
  Deep learning models have reached or even exceeded human-level performance in a range of visual perception tasks. Deep learning models usually demand significant computational resources, leading to impractical power consumption, latency, or carbon emissions in real-world scenarios. New research focus is computationally efficient deep learning, which strives to achieve satisfactory performance while minimizing the computational cost during inference.
  arXiv  Detail & Related papers  (2023-08-27T03:55:28Z)
• Photonic reservoir computing enabled by stimulated Brillouin scattering [0.0]
  A new computing platform based on the photonic reservoir computing architecture exploiting the non-linear wave-optical dynamics of the stimulated Brillouin scattering is reported. It is readily suited for use in conjunction with high performance optical multiplexing techniques to enable real-time artificial intelligence.
  arXiv  Detail & Related papers  (2023-02-15T14:57:30Z)
• All-optical graph representation learning using integrated diffractive photonic computing units [51.15389025760809]
  Photonic neural networks perform brain-inspired computations using photons instead of electrons. We propose an all-optical graph representation learning architecture, termed diffractive graph neural network (DGNN) We demonstrate the use of DGNN extracted features for node and graph-level classification tasks with benchmark databases and achieve superior performance.
  arXiv  Detail & Related papers  (2022-04-23T02:29:48Z)
• Photonics for artificial intelligence and neuromorphic computing [52.77024349608834]
  Photonic integrated circuits have enabled ultrafast artificial neural networks. Photonic neuromorphic systems offer sub-nanosecond latencies. These systems could address the growing demand for machine learning and artificial intelligence.
  arXiv  Detail & Related papers  (2020-10-30T21:41:44Z)
• Rapid characterisation of linear-optical networks via PhaseLift [51.03305009278831]
  Integrated photonics offers great phase-stability and can rely on the large scale manufacturability provided by the semiconductor industry. New devices, based on such optical circuits, hold the promise of faster and energy-efficient computations in machine learning applications. We present a novel technique to reconstruct the transfer matrix of linear optical networks.
  arXiv  Detail & Related papers  (2020-10-01T16:04:22Z)
• Spiking Neural Networks Hardware Implementations and Challenges: a Survey [53.429871539789445]
  Spiking Neural Networks are cognitive algorithms mimicking neuron and synapse operational principles. We present the state of the art of hardware implementations of spiking neural networks. We discuss the strategies employed to leverage the characteristics of these event-driven algorithms at the hardware level.
  arXiv  Detail & Related papers  (2020-05-04T13:24:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.