OptINC: Optical In-Network-Computing for Scalable Distributed Learning

• URL: http://arxiv.org/abs/2603.28290v1
• Date: Mon, 30 Mar 2026 11:10:47 GMT
• Title: OptINC: Optical In-Network-Computing for Scalable Distributed Learning
• Abstract summary: We propose an Optical In-Network-Computing (INC) architecture to offload the computation in servers onto the optical interconnects.<n>Such a de facto optical neural network (ONN) can effectively reduce the communication overhead in existing distributed training solutions.<n>The proposed solution was evaluated on real distributed learning tasks, including ResNet50 on CIFAR-100, and a LLaMA-based network on Wikipedia-1B.
• Score: 8.207258785260722
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Distributed learning is widely used for training large models on large datasets by distributing parts of the model or dataset across multiple devices and aggregating the computed results for subsequent computations or parameter updates. Existing communication algorithms for distributed learning such as ring all-reduce result in heavy communication overhead between servers. Since communication in large-scale systems uses optical fibers, we propose an Optical In-Network-Computing (OptINC) architecture to offload the computation in servers onto the optical interconnects. To execute gradient averaging and quantization in the optical domain, we incorporate optical devices such as Mach-Zehnder-Interferometers (MZIs) into the interconnects. Such a de facto optical neural network (ONN) can effectively reduce the communication overhead in existing distributed training solutions. To reduce dataset complexity for training this neural network, a preprocessing algorithm implemented in the optical domain is also proposed. Hardware cost is lowered by approximating the weight matrices of the optical neural network with unitary and diagonal matrices, while the accuracy is maintained by a proposed hardware-aware training algorithm. The proposed solution was evaluated on real distributed learning tasks, including ResNet50 on CIFAR-100, and a LLaMA-based network on Wikipedia-1B. In both cases, the proposed framework can achieve comparable training accuracy to the ring all-reduce baseline, while eliminating communication overhead.

Related papers

• Federated Split Learning with Model Pruning and Gradient Quantization in Wireless Networks [7.439160287320074]
  Federated split learning (FedSL) implements collaborative training across the edge devices and the server through model splitting.<n>We propose a lightweight FedSL scheme, that further alleviates the training burden on resource-constrained edge devices.<n>We conduct theoretical analysis to quantify the convergence performance of the proposed scheme.
  arXiv  Detail & Related papers  (2024-12-09T11:43:03Z)
• Neuromorphic Wireless Split Computing with Multi-Level Spikes [69.73249913506042]
  Neuromorphic computing uses spiking neural networks (SNNs) to perform inference tasks.<n> embedding a small payload within each spike exchanged between spiking neurons can enhance inference accuracy without increasing energy consumption.<n> split computing - where an SNN is partitioned across two devices - is a promising solution.<n>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 Large-Scale Optical Neural Networks with Two-Pass Forward Propagation [0.0]
  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.
  arXiv  Detail & Related papers  (2024-08-15T11:27:01Z)
• Sophisticated deep learning with on-chip optical diffractive tensor processing [5.081061839052458]
  Photonic integrated circuits provide an efficient approach to mitigate bandwidth limitations and power-wall brought by electronic counterparts. We propose an optical computing architecture enabled by on-chip diffraction to implement convolutional acceleration, termed optical convolution unit (OCU) With OCU as the fundamental unit, we build an optical convolutional neural network (oCNN) to implement two popular deep learning tasks: classification and regression.
  arXiv  Detail & Related papers  (2022-12-20T03:33:26Z)
• Predictive GAN-powered Multi-Objective Optimization for Hybrid Federated Split Learning [56.125720497163684]
  We propose a hybrid federated split learning framework in wireless networks. We design a parallel computing scheme for model splitting without label sharing, and theoretically analyze the influence of the delayed gradient caused by the scheme on the convergence speed.
  arXiv  Detail & Related papers  (2022-09-02T10:29:56Z)
• Neural Architecture Search for Improving Latency-Accuracy Trade-off in Split Computing [5.516431145236317]
  Split computing is an emerging machine-learning inference technique that addresses the privacy and latency challenges of deploying deep learning in IoT systems. In split computing, neural network models are separated and cooperatively processed using edge servers and IoT devices via networks. This paper proposes a neural architecture search (NAS) method for split computing.
  arXiv  Detail & Related papers  (2022-08-30T03:15:43Z)
• An Adaptive Device-Edge Co-Inference Framework Based on Soft Actor-Critic [72.35307086274912]
  High-dimension parameter model and large-scale mathematical calculation restrict execution efficiency, especially for Internet of Things (IoT) devices. We propose a new Deep Reinforcement Learning (DRL)-Soft Actor Critic for discrete (SAC-d), which generates the emphexit point, emphexit point, and emphcompressing bits by soft policy iterations. Based on the latency and accuracy aware reward design, such an computation can well adapt to the complex environment like dynamic wireless channel and arbitrary processing, and is capable of supporting the 5G URL
  arXiv  Detail & Related papers  (2022-01-09T09:31:50Z)
• Collaborative Learning over Wireless Networks: An Introductory Overview [84.09366153693361]
  We will mainly focus on collaborative training across wireless devices. Many distributed optimization algorithms have been developed over the last decades. They provide data locality; that is, a joint model can be trained collaboratively while the data available at each participating device remains local.
  arXiv  Detail & Related papers  (2021-12-07T20:15:39Z)
• ItNet: iterative neural networks with small graphs for accurate and efficient anytime prediction [1.52292571922932]
  In this study, we introduce a class of network models that have a small memory footprint in terms of their computational graphs. We show state-of-the-art results for semantic segmentation on the CamVid and Cityscapes datasets.
  arXiv  Detail & Related papers  (2021-01-21T15:56:29Z)
• A Compressive Sensing Approach for Federated Learning over Massive MIMO Communication Systems [82.2513703281725]
  Federated learning is a privacy-preserving approach to train a global model at a central server by collaborating with wireless devices. We present a compressive sensing approach for federated learning over massive multiple-input multiple-output communication systems.
  arXiv  Detail & Related papers  (2020-03-18T05:56:27Z)
• Channel Assignment in Uplink Wireless Communication using Machine Learning Approach [54.012791474906514]
  This letter investigates a channel assignment problem in uplink wireless communication systems. Our goal is to maximize the sum rate of all users subject to integer channel assignment constraints. Due to high computational complexity, machine learning approaches are employed to obtain computational efficient solutions.
  arXiv  Detail & Related papers  (2020-01-12T15:54:20Z)

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.