The Promise of Spiking Neural Networks for Ubiquitous Computing: A Survey and New Perspectives

• URL: http://arxiv.org/abs/2506.01737v1
• Date: Mon, 02 Jun 2025 14:47:48 GMT
• Title: The Promise of Spiking Neural Networks for Ubiquitous Computing: A Survey and New Perspectives
• Authors: Hemanth Sabbella, Archit Mukherjee, Thivya Kandappu, Sounak Dey, Arpan Pal, Archan Misra, Dong Ma,
• Abstract summary: 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.
• Score: 6.0751106418587835
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Spiking neural networks (SNNs) have emerged as a class of bio -inspired networks that leverage sparse, event-driven signaling to achieve low-power computation while inherently modeling temporal dynamics. Such characteristics align closely with the demands of ubiquitous computing systems, which often operate on resource-constrained devices while continuously monitoring and processing time-series sensor data. Despite their unique and promising features, SNNs have received limited attention and remain underexplored (or at least, under-adopted) within the ubiquitous computing community. To address this gap, this paper first introduces the core components of SNNs, both in terms of models and training mechanisms. It then presents a systematic survey of 76 SNN-based studies focused on time-series data analysis, categorizing them into six key application domains. For each domain, we summarize relevant works and subsequent advancements, distill core insights, and highlight key takeaways for researchers and practitioners. To facilitate hands-on experimentation, we also provide a comprehensive review of current software frameworks and neuromorphic hardware platforms, detailing their capabilities and specifications, and then offering tailored recommendations for selecting development tools based on specific application needs. Finally, we identify prevailing challenges within each application domain and propose future research directions that need be explored in ubiquitous community. Our survey highlights the transformative potential of SNNs in enabling energy-efficient ubiquitous sensing across diverse application domains, while also serving as an essential introduction for researchers looking to enter this emerging field.

Related papers

• Edge Intelligence with Spiking Neural Networks [50.33340747216377]
  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.
  arXiv  Detail & Related papers  (2025-07-18T16:47:52Z)
• Spiking Neural Networks for Temporal Processing: Status Quo and Future Prospects [41.8742357294068]
  Spiking Neural Networks (SNNs) excel in handling data with high efficiency, owing to their rich neuronal dynamics and sparse activity patterns.<n>Given the recent surge in the development of SNNs, there is an urgent need for a comprehensive evaluation of their temporal processing capabilities.
  arXiv  Detail & Related papers  (2025-02-13T16:17:57Z)
• Continual Learning with Hebbian Plasticity in Sparse and Predictive Coding Networks: A Survey and Perspective [1.3986052523534573]
  An emerging class of neuromorphic continual learning systems must learn to integrate new information on the fly. This survey covers a number of recent works in the field of neuromorphic continual learning based on state-of-the-art Sparse and Predictive Coding technology. It is hoped that this survey will contribute towards future research in the field of neuromorphic continual learning.
  arXiv  Detail & Related papers  (2024-07-24T14:20:59Z)
• NNsight and NDIF: Democratizing Access to Open-Weight Foundation Model Internals [58.83169560132308]
  We introduce NNsight and NDIF, technologies that work in tandem to enable scientific study of the representations and computations learned by very large neural networks.
  arXiv  Detail & Related papers  (2024-07-18T17:59:01Z)
• Efficient and Effective Time-Series Forecasting with Spiking Neural Networks [47.371024581669516]
  Spiking neural networks (SNNs) provide a unique pathway for capturing the intricacies of temporal data. Applying SNNs to time-series forecasting is challenging due to difficulties in effective temporal alignment, complexities in encoding processes, and the absence of standardized guidelines for model selection. We propose a framework for SNNs in time-series forecasting tasks, leveraging the efficiency of spiking neurons in processing temporal information.
  arXiv  Detail & Related papers  (2024-02-02T16:23:50Z)
• A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection [98.41798478488101]
  Time series analytics is crucial to unlocking the wealth of information implicit in available data. Recent advancements in graph neural networks (GNNs) have led to a surge in GNN-based approaches for time series analysis. This survey brings together a vast array of knowledge on GNN-based time series research, highlighting foundations, practical applications, and opportunities of graph neural networks for time series analysis.
  arXiv  Detail & Related papers  (2023-07-07T08:05:03Z)
• Graph Neural Network for spatiotemporal data: methods and applications [7.612070518526342]
  Graph neural networks (GNNs) have emerged as a powerful tool for understanding data with dependencies to each other. This article aims to provide an overview of the technologies and applications of GNNs in thetemporal domain.
  arXiv  Detail & Related papers  (2023-05-30T02:27:17Z)
• Fluid Batching: Exit-Aware Preemptive Serving of Early-Exit Neural Networks on Edge NPUs [74.83613252825754]
  "smart ecosystems" are being formed where sensing happens concurrently rather than standalone. This is shifting the on-device inference paradigm towards deploying neural processing units (NPUs) at the edge. We propose a novel early-exit scheduling that allows preemption at run time to account for the dynamicity introduced by the arrival and exiting processes.
  arXiv  Detail & Related papers  (2022-09-27T15:04:01Z)
• Distributed intelligence on the Edge-to-Cloud Continuum: A systematic literature review [62.997667081978825]
  This review aims at providing a comprehensive vision of the main state-of-the-art libraries and frameworks for machine learning and data analytics available today. The main simulation, emulation, deployment systems, and testbeds for experimental research on the Edge-to-Cloud Continuum available today are also surveyed.
  arXiv  Detail & Related papers  (2022-04-29T08:06:05Z)
• A Survey of Complex-Valued Neural Networks [4.211128681972148]
  Artificial neural networks (ANNs) based machine learning models have been widely applied in computer vision, signal processing, wireless communications, and many other domains. Most of the current implementations of ANNs and machine learning frameworks are using real numbers rather than complex numbers. There are growing interests in building ANNs using complex numbers, and exploring the potential advantages of the so-called complex-valued neural networks (CVNNs) over their real-valued counterparts.
  arXiv  Detail & Related papers  (2021-01-28T19:40:50Z)
• Graph Neural Networks for Leveraging Industrial Equipment Structure: An application to Remaining Useful Life Estimation [21.297461316329453]
  We propose to capture the structure of a complex equipment in the form of a graph, and use graph neural networks (GNNs) to model multi-sensor time-series data. We observe that the proposed GNN-based RUL estimation model compares favorably to several strong baselines from literature such as those based on RNNs and CNNs.
  arXiv  Detail & Related papers  (2020-06-30T06:38:08Z)
• 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)

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.