Neuromorphic Event-Driven Semantic Communication in Microgrids
- URL: http://arxiv.org/abs/2402.18390v1
- Date: Wed, 28 Feb 2024 15:11:02 GMT
- Title: Neuromorphic Event-Driven Semantic Communication in Microgrids
- Authors: Xiaoguang Diao, Yubo Song, Subham Sahoo, Yuan Li
- Abstract summary: 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.
- Score: 5.817656520396958
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Synergies between advanced communications, computing and artificial
intelligence are unraveling new directions of coordinated operation and
resiliency in microgrids. On one hand, coordination among sources is
facilitated by distributed, privacy-minded processing at multiple locations,
whereas on the other hand, it also creates exogenous data arrival paths for
adversaries that can lead to cyber-physical attacks amongst other reliability
issues in the communication layer. This long-standing problem necessitates new
intrinsic ways of exchanging information between converters through power lines
to optimize the system's control performance. Going beyond the existing power
and data co-transfer technologies that are limited by efficiency and
scalability concerns, this paper proposes neuromorphic learning to implant
communicative features using spiking neural networks (SNNs) at each node, which
is trained collaboratively in an online manner simply using the power exchanges
between the nodes. 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. Finally, its multi-fold effectiveness
and reliable performance is validated under simulation conditions with
different microgrid topologies and components to establish a new direction in
the sense-actuate-compute cycle for power electronic dominated grids and
microgrids.
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) - On Noise Resiliency of Neuromorphic Inferential Communication in Microgrids [1.8529626486588364]
This article explores the noise resiliency of neuromorphic inferential communication in microgrids through case studies.
It offers insights for its implementation in real-world scenarios.
arXiv Detail & Related papers (2024-07-25T17:27:50Z) - Inferring Ingrained Remote Information in AC Power Flows Using Neuromorphic Modality Regime [1.8529626486588364]
This work unifies power and information as a means of data normalization using a multi-modal regime in the form of spikes.
We exploit the latency-driven unsupervised Hebbian learning rule to obtain modulation pulses for switching of power electronic converters.
arXiv Detail & Related papers (2024-07-20T14:20:22Z) - Neuromorphic Split Computing with Wake-Up Radios: Architecture and Design via Digital Twinning [97.99077847606624]
This work proposes a novel architecture that integrates a wake-up radio mechanism within a split computing system consisting of remote, wirelessly connected, NPUs.
A key challenge in the design of a wake-up radio-based neuromorphic split computing system is the selection of thresholds for sensing, wake-up signal detection, and decision making.
arXiv Detail & Related papers (2024-04-02T10:19:04Z) - Leveraging Low-Rank and Sparse Recurrent Connectivity for Robust
Closed-Loop Control [63.310780486820796]
We show how a parameterization of recurrent connectivity influences robustness in closed-loop settings.
We find that closed-form continuous-time neural networks (CfCs) with fewer parameters can outperform their full-rank, fully-connected counterparts.
arXiv Detail & Related papers (2023-10-05T21:44:18Z) - Neuromorphic Wireless Cognition: Event-Driven Semantic Communications
for Remote Inference [32.0035037154674]
This paper proposes an end-to-end design for a neuromorphic wireless Internet-of-Things system.
Each sensing device is equipped with a neuromorphic sensor, a spiking neural network (SNN), and an impulse radio transmitter with multiple antennas.
Pilots, encoding SNNs, decoding SNN, and hypernetwork are jointly trained across multiple channel realizations.
arXiv Detail & Related papers (2022-06-13T11:13:39Z) - Hybrid SNN-ANN: Energy-Efficient Classification and Object Detection for
Event-Based Vision [64.71260357476602]
Event-based vision sensors encode local pixel-wise brightness changes in streams of events rather than image frames.
Recent progress in object recognition from event-based sensors has come from conversions of deep neural networks.
We propose a hybrid architecture for end-to-end training of deep neural networks for event-based pattern recognition and object detection.
arXiv Detail & Related papers (2021-12-06T23:45:58Z) - Learning Autonomy in Management of Wireless Random Networks [102.02142856863563]
This paper presents a machine learning strategy that tackles a distributed optimization task in a wireless network with an arbitrary number of randomly interconnected nodes.
We develop a flexible deep neural network formalism termed distributed message-passing neural network (DMPNN) with forward and backward computations independent of the network topology.
arXiv Detail & Related papers (2021-06-15T09:03:28Z) - Supervised training of spiking neural networks for robust deployment on
mixed-signal neuromorphic processors [2.6949002029513167]
Mixed-signal analog/digital electronic circuits can emulate spiking neurons and synapses with extremely high energy efficiency.
Mismatch is expressed as differences in effective parameters between identically-configured neurons and synapses.
We present a supervised learning approach that addresses this challenge by maximizing robustness to mismatch and other common sources of noise.
arXiv Detail & Related papers (2021-02-12T09:20:49Z) - Spiking Neural Networks -- Part III: Neuromorphic Communications [38.518936229794214]
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.
arXiv Detail & Related papers (2020-10-27T11:52:35Z) - 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)
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.