ECC-SNN: Cost-Effective Edge-Cloud Collaboration for Spiking Neural Networks

2025-05-27

AI Summary

To address high communication overhead, strong cloud dependency, and low energy efficiency in edge–cloud collaboration, this paper proposes an energy-efficient co-processing framework tailored for resource-constrained edge devices. Methodologically, it introduces (1) a novel joint training mechanism integrating artificial neural networks (ANNs) and spiking neural networks (SNNs) to achieve both model lightweighting and high accuracy, and (2) an edge-side incremental learning algorithm enabling online adaptation and continual optimization under dynamic environments. By offloading the majority of computation to the edge, the framework significantly reduces edge–cloud communication and energy consumption. Evaluated on four benchmark datasets, it achieves an average accuracy improvement of 4.15%, a 79.4% reduction in energy consumption, and a 39.1% decrease in processing latency. These results substantially enhance edge autonomy, real-time responsiveness, and operational sustainability.

Technology Category

• Machine Learning: Learning on the Edge & Model Compression
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Computer Vision: Learning & Optimization for CV

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Cloud, edge and content delivery systems for the Web
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Social networks and social learning

Abstract

Most edge-cloud collaboration frameworks rely on the substantial computational and storage capabilities of cloud-based artificial neural networks (ANNs). However, this reliance results in significant communication overhead between edge devices and the cloud and high computational energy consumption, especially when applied to resource-constrained edge devices. To address these challenges, we propose ECC-SNN, a novel edge-cloud collaboration framework incorporating energy-efficient spiking neural networks (SNNs) to offload more computational workload from the cloud to the edge, thereby improving cost-effectiveness and reducing reliance on the cloud. ECC-SNN employs a joint training approach that integrates ANN and SNN models, enabling edge devices to leverage knowledge from cloud models for enhanced performance while reducing energy consumption and processing latency. Furthermore, ECC-SNN features an on-device incremental learning algorithm that enables edge models to continuously adapt to dynamic environments, reducing the communication overhead and resource consumption associated with frequent cloud update requests. Extensive experimental results on four datasets demonstrate that ECC-SNN improves accuracy by 4.15%, reduces average energy consumption by 79.4%, and lowers average processing latency by 39.1%.

Problem

• Reduces communication overhead in edge-cloud collaboration
• Lowers energy consumption for resource-constrained edge devices
• Enables edge models to adapt dynamically with incremental learning

Innovation

• Energy-efficient SNNs for edge-cloud collaboration
• Joint ANN-SNN training for enhanced performance
• On-device incremental learning for dynamic adaptation