ECC-SNN: Cost-Effective Edge-Cloud Collaboration for Spiking Neural Networks
Authors: Di Yu, Changze Lv, Xin Du, Linshan Jiang, Wentao Tong, Zhenyu Liao, Xiaoqing Zheng, Shuiguang Deng
IJCAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 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%. |
| Researcher Affiliation | Academia | 1Zhejiang University 2Fudan University 3National University of Singapore EMAIL, EMAIL, EMAIL, EMAIL EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 Collaborative Inference in ECC-SNN |
| Open Source Code | Yes | 1https://github.com/AmazingDD/ECC-SNN |
| Open Datasets | Yes | Extensive experiments on four diverse datasets demonstrate that ECC-SNN significantly outperforms standalone edge-based SNNs and cloud-based ANNs, achieving an average accuracy improvement of 4.15%, a 79.4% reduction in energy consumption, and a 39.1% decrease in processing latency. (The datasets are identified as CIFAR-10, CIFAR-100, Caltech, Tiny-Image Net in Table 1). |
| Dataset Splits | Yes | Following [Zhou et al., 2024a], we evaluate the effectiveness of our proposed ECC-SNN using the standard classincremental learning setting... We denote the data split setting as w/ B-u, Inc-v, i.e., the first dataset contains u classes, and each following dataset contains v classes. u = 0 means the total classes are equally divided into each task. |
| Hardware Specification | No | By default, we adopt the spiking VGG-9 model as the SNN deployed on the edge device featuring a neuromorphic chip [Ma et al., 2024] while utilizing a widely recognized pretrained Vi T model vit-base-patch16 as the cloud-based ANN. The paper mentions a 'neuromorphic chip' and 'server GPU' but does not specify their exact models or other detailed specifications. |
| Software Dependencies | No | The paper does not explicitly mention specific software dependencies with version numbers. |
| Experiment Setup | Yes | By default, we adopt the spiking VGG-9 model as the SNN deployed on the edge device... while utilizing a widely recognized pretrained Vi T model vit-base-patch16 as the cloud-based ANN. Also, the filtering criterion uses a 'fixed filtering threshold δ=0.3' as indicated in Figures 4, 6, and 7. |