Artificial Neural Networks for Photonic Applications: From Algorithms to Implementation

• URL: http://arxiv.org/abs/2408.02685v1
• Date: Fri, 2 Aug 2024 08:22:49 GMT
• Title: Artificial Neural Networks for Photonic Applications: From Algorithms to Implementation
• Authors: Pedro Freire, Egor Manuylovich, Jaroslaw E. Prilepsky, Sergei K. Turitsy,
• Abstract summary: 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.
• Score: 1.7371307928431834
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This tutorial-review on applications of artificial neural networks in photonics targets a broad audience, ranging from optical research and engineering communities to computer science and applied mathematics. 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. First, we briefly recall key properties and peculiarities of some core neural network types, which we believe are the most relevant to photonics, also linking the layer's theoretical design to some photonics hardware realizations. After that, we elucidate the question of how to fine-tune the selected model's design to perform the required task with optimized accuracy. Then, in the review part, we discuss recent developments and progress for several selected applications of neural networks in photonics, including multiple aspects relevant to optical communications, imaging, sensing, and the design of new materials and lasers. In the following section, we put a special emphasis on how to accurately evaluate the complexity of neural networks in the context of the transition from algorithms to hardware implementation. The introduced complexity characteristics are used to analyze the applications of neural networks in optical communications, as a specific, albeit highly important example, comparing those with some benchmark signal processing methods. We combine the description of the well-known model compression strategies used in machine learning, with some novel techniques introduced recently in optical applications of neural networks. It is important to stress that although our focus in this tutorial-review is on photonics, we believe that the methods and techniques presented here can be handy in a much wider range of scientific and engineering applications.

Related papers

• Genetically programmable optical random neural networks [0.0]
  We demonstrate a genetically programmable yet simple optical neural network to achieve high performances with optical random projection. By genetically programming the orientation of the scattering medium which acts as a random projection kernel, our novel technique finds an optimum kernel and improves its initial test accuracies 7-22%. Our optical computing method presents a promising approach to achieve high performance in optical neural networks with a simple and scalable design.
  arXiv  Detail & Related papers  (2024-03-19T06:55:59Z)
• Graph Neural Networks for Learning Equivariant Representations of Neural Networks [55.04145324152541]
  We propose to represent neural networks as computational graphs of parameters. Our approach enables a single model to encode neural computational graphs with diverse architectures. We showcase the effectiveness of our method on a wide range of tasks, including classification and editing of implicit neural representations.
  arXiv  Detail & Related papers  (2024-03-18T18:01:01Z)
• Conditional computation in neural networks: principles and research trends [48.14569369912931]
  This article summarizes principles and ideas from the emerging area of applying textitconditional computation methods to the design of neural networks. In particular, we focus on neural networks that can dynamically activate or de-activate parts of their computational graph conditionally on their input.
  arXiv  Detail & Related papers  (2024-03-12T11:56:38Z)
• 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)
• Free-Space Optical Spiking Neural Network [0.0]
  We introduce the Free-space Optical deep Spiking Convolutional Neural Network (OSCNN) This novel approach draws inspiration from computational models of the human eye. Our results demonstrate promising performance with minimal latency and power consumption compared to their electronic ONN counterparts.
  arXiv  Detail & Related papers  (2023-11-08T09:41:14Z)
• Analog Photonics Computing for Information Processing, Inference and Optimisation [0.0]
  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.
  arXiv  Detail & Related papers  (2023-01-27T14:58:22Z)
• Experimentally realized in situ backpropagation for deep learning in nanophotonic neural networks [0.7627023515997987]
  We design mass-manufacturable silicon photonic neural networks that cascade our custom designed "photonic mesh" accelerator. We demonstrate in situ backpropagation for the first time to solve classification tasks. Our findings suggest a new training paradigm for photonics-accelerated artificial intelligence based entirely on a physical analog of the popular backpropagation technique.
  arXiv  Detail & Related papers  (2022-05-17T17:13:50Z)
• 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)
• NAS-Navigator: Visual Steering for Explainable One-Shot Deep Neural Network Synthesis [53.106414896248246]
  We present a framework that allows analysts to effectively build the solution sub-graph space and guide the network search by injecting their domain knowledge. Applying this technique in an iterative manner allows analysts to converge to the best performing neural network architecture for a given application.
  arXiv  Detail & Related papers  (2020-09-28T01:48:45Z)
• Deep neural networks for the evaluation and design of photonic devices [0.0]
  Review: How deep neural networks can learn from training sets and operate as high-speed surrogate electromagnetic solvers. Fundamental data sciences framed within the context of photonics will also be discussed.
  arXiv  Detail & Related papers  (2020-06-30T19:52:54Z)
• 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.