Related papers: Spiking Neural Networks -- Part III: Neuromorphic Communications

Spiking Neural Networks -- Part III: Neuromorphic Communications

URL: http://arxiv.org/abs/2010.14220v2
Date: Wed, 9 Dec 2020 17:18:15 GMT
Title: Spiking Neural Networks -- Part III: Neuromorphic Communications
Authors: Nicolas Skatchkovsky, Hyeryung Jang, Osvaldo Simeone
Abstract summary: The presence of more and more wirelessly connected devices is driving efforts to export advances in machine learning. Implementing machine learning models for learning and inference on battery-powered devices that are connected via bandwidth-constrained channels remains challenging. This paper explores two ways in which Spiking Neural Networks (SNNs) can help address these open problems.
Score: 38.518936229794214
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Synergies between wireless communications and artificial intelligence are increasingly motivating research at the intersection of the two fields. On the one hand, the presence of more and more wirelessly connected devices, each with its own data, is driving efforts to export advances in machine learning (ML) from high performance computing facilities, where information is stored and processed in a single location, to distributed, privacy-minded, processing at the end user. On the other hand, ML can address algorithm and model deficits in the optimization of communication protocols. However, implementing ML models for learning and inference on battery-powered devices that are connected via bandwidth-constrained channels remains challenging. This paper explores two ways in which Spiking Neural Networks (SNNs) can help address these open problems. First, we discuss federated learning for the distributed training of SNNs, and then describe the integration of neuromorphic sensing, SNNs, and impulse radio technologies for low-power remote inference.

Related papers

Neuromorphic Wireless Split Computing with Multi-Level Spikes [69.73249913506042]
In neuromorphic computing, spiking neural networks (SNNs) perform inference tasks, offering significant efficiency gains for workloads involving sequential data. Recent advances in hardware and software have demonstrated that embedding a few bits of payload in each spike exchanged between the spiking neurons can further enhance inference accuracy. This paper investigates a wireless neuromorphic split computing architecture employing multi-level SNNs.
arXiv Detail & Related papers (2024-11-07T14:08:35Z)
Neuromorphic Event-Driven Semantic Communication in Microgrids [5.817656520396958]
This paper proposes neuromorphic learning to implant communicative features using spiking neural networks (SNNs) at each node. As opposed to the conventional neuromorphic sensors that operate with spiking signals, we employ an event-driven selective process to collect sparse data for training of SNNs.
arXiv Detail & Related papers (2024-02-28T15:11:02Z)
Neuromorphic Integrated Sensing and Communications [28.475406916976247]
We introduce neuromorphic integrated sensing and communications (N-ISAC), a novel solution that enables efficient online data decoding and radar sensing. N-ISAC leverages a common IR waveform for the dual purpose of conveying digital information and of detecting the presence or absence of a radar target. A spiking neural network (SNN) is deployed at the receiver to decode digital data and detect the radar target using directly the received signal.
arXiv Detail & Related papers (2022-09-24T00:23:25Z)
Communication-Efficient Separable Neural Network for Distributed Inference on Edge Devices [2.28438857884398]
We propose a novel method of exploiting model parallelism to separate a neural network for distributed inferences. Under proper specifications of devices and configurations of models, our experiments show that the inference of large neural networks on edge clusters can be distributed and accelerated.
arXiv Detail & Related papers (2021-11-03T19:30:28Z)
Distributed Learning in Wireless Networks: Recent Progress and Future Challenges [170.35951727508225]
Next-generation wireless networks will enable many machine learning (ML) tools and applications to analyze various types of data collected by edge devices. Distributed learning and inference techniques have been proposed as a means to enable edge devices to collaboratively train ML models without raw data exchanges. This paper provides a comprehensive study of how distributed learning can be efficiently and effectively deployed over wireless edge networks.
arXiv Detail & Related papers (2021-04-05T20:57:56Z)
Communication-Efficient and Distributed Learning Over Wireless Networks: Principles and Applications [55.65768284748698]
Machine learning (ML) is a promising enabler for the fifth generation (5G) communication systems and beyond. This article aims to provide a holistic overview of relevant communication and ML principles, and thereby present communication-efficient and distributed learning frameworks with selected use cases.
arXiv Detail & Related papers (2020-08-06T12:37:14Z)
From Federated to Fog Learning: Distributed Machine Learning over Heterogeneous Wireless Networks [71.23327876898816]
Federated learning has emerged as a technique for training ML models at the network edge by leveraging processing capabilities across the nodes that collect the data. We advocate a new learning paradigm called fog learning which will intelligently distribute ML model training across the continuum of nodes from edge devices to cloud servers.
arXiv Detail & Related papers (2020-06-07T05:11:18Z)
Deep Learning for Ultra-Reliable and Low-Latency Communications in 6G Networks [84.2155885234293]
We first summarize how to apply data-driven supervised deep learning and deep reinforcement learning in URLLC. To address these open problems, we develop a multi-level architecture that enables device intelligence, edge intelligence, and cloud intelligence for URLLC.
arXiv Detail & Related papers (2020-02-22T14:38:11Z)

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