Edge Intelligence with Spiking Neural Networks

• URL: http://arxiv.org/abs/2507.14069v1
• Date: Fri, 18 Jul 2025 16:47:52 GMT
• Title: Edge Intelligence with Spiking Neural Networks
• Authors: Shuiguang Deng, Di Yu, Changze Lv, Xin Du, Linshan Jiang, Xiaofan Zhao, Wentao Tong, Xiaoqing Zheng, Weijia Fang, Peng Zhao, Gang Pan, Schahram Dustdar, Albert Y. Zomaya,
• Abstract summary: Spiking Neural Networks (SNNs) offer low-power, event-driven computation on resource-constrained devices.<n>We present a systematic taxonomy of EdgeSNN foundations, encompassing neuron models, learning algorithms, and supporting hardware platforms.<n>Three representative practical considerations of EdgeSNN are discussed in depth: on-device inference using lightweight SNN models, resource-aware training and updating under non-stationary data conditions, and secure and privacy-preserving issues.
• Score: 50.33340747216377
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The convergence of artificial intelligence and edge computing has spurred growing interest in enabling intelligent services directly on resource-constrained devices. While traditional deep learning models require significant computational resources and centralized data management, the resulting latency, bandwidth consumption, and privacy concerns have exposed critical limitations in cloud-centric paradigms. Brain-inspired computing, particularly Spiking Neural Networks (SNNs), offers a promising alternative by emulating biological neuronal dynamics to achieve low-power, event-driven computation. This survey provides a comprehensive overview of Edge Intelligence based on SNNs (EdgeSNNs), examining their potential to address the challenges of on-device learning, inference, and security in edge scenarios. We present a systematic taxonomy of EdgeSNN foundations, encompassing neuron models, learning algorithms, and supporting hardware platforms. Three representative practical considerations of EdgeSNN are discussed in depth: on-device inference using lightweight SNN models, resource-aware training and updating under non-stationary data conditions, and secure and privacy-preserving issues. Furthermore, we highlight the limitations of evaluating EdgeSNNs on conventional hardware and introduce a dual-track benchmarking strategy to support fair comparisons and hardware-aware optimization. Through this study, we aim to bridge the gap between brain-inspired learning and practical edge deployment, offering insights into current advancements, open challenges, and future research directions. To the best of our knowledge, this is the first dedicated and comprehensive survey on EdgeSNNs, providing an essential reference for researchers and practitioners working at the intersection of neuromorphic computing and edge intelligence.

Related papers

• The Promise of Spiking Neural Networks for Ubiquitous Computing: A Survey and New Perspectives [6.0751106418587835]
  Spiking neural networks (SNNs) have emerged as a class of bio-inspired networks that leverage sparse, event-driven signaling to achieve low-power computation.<n>Despite their unique and promising features, SNNs have received limited attention and remain underexplored within the ubiquitous computing community.
  arXiv  Detail & Related papers  (2025-06-02T14:47:48Z)
• Edge-Cloud Collaborative Computing on Distributed Intelligence and Model Optimization: A Survey [59.52058740470727]
  Edge-cloud collaborative computing (ECCC) has emerged as a pivotal paradigm for addressing the computational demands of modern intelligent applications.<n>Recent advancements in AI, particularly deep learning and large language models (LLMs), have dramatically enhanced the capabilities of these distributed systems.<n>This survey provides a structured tutorial on fundamental architectures, enabling technologies, and emerging applications.
  arXiv  Detail & Related papers  (2025-05-03T13:55:38Z)
• On Achieving Privacy-Preserving State-of-the-Art Edge Intelligence [0.0]
  Inference in Edge Computing, often called Edge Intelligence, requires solutions to insure that sensitive data confidentiality and intellectual property are not revealed in the process. This paper provides an original assessment of the compatibility of existing techniques for privacy-preserving DNN Inference with the characteristics of an Edge Computing setup. We then address the future role of model compression methods in the research towards secret sharing on DNNs with state-of-the-art performance.
  arXiv  Detail & Related papers  (2023-02-10T15:34:42Z)
• A Survey on Collaborative DNN Inference for Edge Intelligence [22.691247982285432]
  Edge intelligence (EI) becomes a cutting-edge direction in the field of AI. In this paper, we classify four typical collaborative DNN inference paradigms for EI, and analyze the characteristics and key technologies of them.
  arXiv  Detail & Related papers  (2022-07-16T02:32:35Z)
• A Review of Recent Advances of Binary Neural Networks for Edge Computing [15.646692826399438]
  This paper reviews advances on binary neural network (BNN) and 1-bit CNN technologies that are well suitable for front-end, edge-based computing. We also introduce applications in the areas of computer vision and speech recognition and discuss future applications for edge computing.
  arXiv  Detail & Related papers  (2020-11-24T01:10:21Z)
• State-of-the-art Techniques in Deep Edge Intelligence [0.0]
  Edge Intelligence (EI) has quickly emerged as a powerful alternative to enable learning using the concepts of Edge Computing. In this article, we provide an overview of the major constraints in operationalizing DEI.
  arXiv  Detail & Related papers  (2020-08-03T12:17:23Z)
• Boosting Deep Neural Networks with Geometrical Prior Knowledge: A Survey [77.99182201815763]
  Deep Neural Networks (DNNs) achieve state-of-the-art results in many different problem settings. DNNs are often treated as black box systems, which complicates their evaluation and validation. One promising field, inspired by the success of convolutional neural networks (CNNs) in computer vision tasks, is to incorporate knowledge about symmetric geometrical transformations.
  arXiv  Detail & Related papers  (2020-06-30T14:56:05Z)
• Incentive Mechanism Design for Resource Sharing in Collaborative Edge Learning [106.51930957941433]
  In 5G and Beyond networks, Artificial Intelligence applications are expected to be increasingly ubiquitous. This necessitates a paradigm shift from the current cloud-centric model training approach to the Edge Computing based collaborative learning scheme known as edge learning.
  arXiv  Detail & Related papers  (2020-05-31T12:45:06Z)
• 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)
• 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.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.