Training Large-Scale Optical Neural Networks with Two-Pass Forward Propagation
- URL: http://arxiv.org/abs/2408.08337v1
- Date: Thu, 15 Aug 2024 11:27:01 GMT
- Title: Training Large-Scale Optical Neural Networks with Two-Pass Forward Propagation
- Authors: Amirreza Ahmadnejad, Somayyeh Koohi,
- Abstract summary: This paper addresses the limitations in Optical Neural Networks (ONNs) related to training efficiency, nonlinear function implementation, and large input data processing.
We introduce Two-Pass Forward Propagation, a novel training method that avoids specific nonlinear activation functions by modulating and re-entering error with random noise.
We propose a new way to implement convolutional neural networks using simple neural networks in integrated optical systems.
- Score: 0.0
- License: http://creativecommons.org/publicdomain/zero/1.0/
- Abstract: This paper addresses the limitations in Optical Neural Networks (ONNs) related to training efficiency, nonlinear function implementation, and large input data processing. We introduce Two-Pass Forward Propagation, a novel training method that avoids specific nonlinear activation functions by modulating and re-entering error with random noise. Additionally, we propose a new way to implement convolutional neural networks using simple neural networks in integrated optical systems. Theoretical foundations and numerical results demonstrate significant improvements in training speed, energy efficiency, and scalability, advancing the potential of optical computing for complex data tasks.
Related papers
- Training Hybrid Neural Networks with Multimode Optical Nonlinearities Using Digital Twins [2.8479179029634984]
We introduce ultrashort pulse propagation in multimode fibers, which perform large-scale nonlinear transformations.
Training the hybrid architecture is achieved through a neural model that differentiably approximates the optical system.
Our experimental results achieve state-of-the-art image classification accuracies and simulation fidelity.
arXiv Detail & Related papers (2025-01-14T10:35:18Z) - Neuromorphic Wireless Split Computing with Multi-Level Spikes [69.73249913506042]
Neuromorphic computing uses spiking neural networks (SNNs) to perform inference tasks.
embedding a small payload within each spike exchanged between spiking neurons can enhance inference accuracy without increasing energy consumption.
split computing - where an SNN is partitioned across two devices - is a promising solution.
This paper presents the first comprehensive study of a neuromorphic wireless split computing architecture that employs multi-level SNNs.
arXiv Detail & Related papers (2024-11-07T14:08:35Z) - Training neural networks with end-to-end optical backpropagation [1.1602089225841632]
We show how to implement backpropagation, an algorithm for training a neural network, using optical processes.
Our approach is adaptable to various analog platforms, materials, and network structures.
It demonstrates the possibility of constructing neural networks entirely reliant on analog optical processes for both training and inference tasks.
arXiv Detail & Related papers (2023-08-09T21:11:26Z) - Forward-Forward Training of an Optical Neural Network [6.311461340782698]
We present an experiment utilizing multimode nonlinear wave propagation in an optical fiber demonstrating the feasibility of the FFA approach using an optical system.
The results show that incorporating optical transforms in multilayer NN architectures trained with the FFA, can lead to performance improvements.
arXiv Detail & Related papers (2023-05-30T16:15:57Z) - Globally Optimal Training of Neural Networks with Threshold Activation
Functions [63.03759813952481]
We study weight decay regularized training problems of deep neural networks with threshold activations.
We derive a simplified convex optimization formulation when the dataset can be shattered at a certain layer of the network.
arXiv Detail & Related papers (2023-03-06T18:59:13Z) - Simple initialization and parametrization of sinusoidal networks via
their kernel bandwidth [92.25666446274188]
sinusoidal neural networks with activations have been proposed as an alternative to networks with traditional activation functions.
We first propose a simplified version of such sinusoidal neural networks, which allows both for easier practical implementation and simpler theoretical analysis.
We then analyze the behavior of these networks from the neural tangent kernel perspective and demonstrate that their kernel approximates a low-pass filter with an adjustable bandwidth.
arXiv Detail & Related papers (2022-11-26T07:41:48Z) - Intelligence Processing Units Accelerate Neuromorphic Learning [52.952192990802345]
Spiking neural networks (SNNs) have achieved orders of magnitude improvement in terms of energy consumption and latency.
We present an IPU-optimized release of our custom SNN Python package, snnTorch.
arXiv Detail & Related papers (2022-11-19T15:44:08Z) - Neural Galerkin Schemes with Active Learning for High-Dimensional
Evolution Equations [44.89798007370551]
This work proposes Neural Galerkin schemes based on deep learning that generate training data with active learning for numerically solving high-dimensional partial differential equations.
Neural Galerkin schemes build on the Dirac-Frenkel variational principle to train networks by minimizing the residual sequentially over time.
Our finding is that the active form of gathering training data of the proposed Neural Galerkin schemes is key for numerically realizing the expressive power of networks in high dimensions.
arXiv Detail & Related papers (2022-03-02T19:09:52Z) - Scale-, shift- and rotation-invariant diffractive optical networks [0.0]
Diffractive Deep Neural Networks (D2NNs) harness light-matter interaction over a series of trainable surfaces to compute a desired statistical inference task.
Here, we demonstrate a new training strategy for diffractive networks that introduces input object translation, rotation and/or scaling during the training phase.
This training strategy successfully guides the evolution of the diffractive optical network design towards a solution that is scale-, shift- and rotation-invariant.
arXiv Detail & Related papers (2020-10-24T02:18:39Z) - Optimization-driven Machine Learning for Intelligent Reflecting Surfaces
Assisted Wireless Networks [82.33619654835348]
Intelligent surface (IRS) has been employed to reshape the wireless channels by controlling individual scattering elements' phase shifts.
Due to the large size of scattering elements, the passive beamforming is typically challenged by the high computational complexity.
In this article, we focus on machine learning (ML) approaches for performance in IRS-assisted wireless networks.
arXiv Detail & Related papers (2020-08-29T08:39:43Z) - Understanding the Effects of Data Parallelism and Sparsity on Neural
Network Training [126.49572353148262]
We study two factors in neural network training: data parallelism and sparsity.
Despite their promising benefits, understanding of their effects on neural network training remains elusive.
arXiv Detail & Related papers (2020-03-25T10:49:22Z)
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.