MetaCLBench: Meta Continual Learning Benchmark on Resource-Constrained Edge Devices
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866917219846324224 |
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| author | Li, Sijia Kwon, Young D. Lee, Lik-Hang Hui, Pan |
| author_facet | Li, Sijia Kwon, Young D. Lee, Lik-Hang Hui, Pan |
| contents | Meta-Continual Learning (Meta-CL) enables models to learn new classes from limited labelled samples, making it promising for IoT applications where manual labelling is costly. However, existing studies focus on accuracy while ignoring deployment viability on resource-constrained hardware. Thus, we present MetaCLBench, a benchmark framework that evaluates Meta-CL methods for both accuracy and deployment-critical metrics (memory footprint, latency, and energy consumption) on real IoT devices with RAM sizes ranging from 512 MB to 4 GB. We evaluate six Meta-CL methods across three architectures (CNN, YAMNet, ViT) and five datasets spanning image and audio modalities. Our evaluation reveals that, depending on the dataset, up to three of six methods cause out-of-memory failures on sub-1 GB devices, significantly narrowing viable deployment options. LifeLearner achieves near-oracle accuracy while consuming 2.54-7.43x less energy than the Oracle method. Notably, larger or more sophisticated architectures such as ViT and YAMNet do not necessarily yield better Meta-CL performance, with results varying across datasets and modalities, challenging conventional assumptions about model complexity. Finally, we provide practical deployment guidelines and will release our framework upon publication to enable fair evaluation across both accuracy and system-level metrics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_00174 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MetaCLBench: Meta Continual Learning Benchmark on Resource-Constrained Edge Devices Li, Sijia Kwon, Young D. Lee, Lik-Hang Hui, Pan Machine Learning Artificial Intelligence Meta-Continual Learning (Meta-CL) enables models to learn new classes from limited labelled samples, making it promising for IoT applications where manual labelling is costly. However, existing studies focus on accuracy while ignoring deployment viability on resource-constrained hardware. Thus, we present MetaCLBench, a benchmark framework that evaluates Meta-CL methods for both accuracy and deployment-critical metrics (memory footprint, latency, and energy consumption) on real IoT devices with RAM sizes ranging from 512 MB to 4 GB. We evaluate six Meta-CL methods across three architectures (CNN, YAMNet, ViT) and five datasets spanning image and audio modalities. Our evaluation reveals that, depending on the dataset, up to three of six methods cause out-of-memory failures on sub-1 GB devices, significantly narrowing viable deployment options. LifeLearner achieves near-oracle accuracy while consuming 2.54-7.43x less energy than the Oracle method. Notably, larger or more sophisticated architectures such as ViT and YAMNet do not necessarily yield better Meta-CL performance, with results varying across datasets and modalities, challenging conventional assumptions about model complexity. Finally, we provide practical deployment guidelines and will release our framework upon publication to enable fair evaluation across both accuracy and system-level metrics. |
| title | MetaCLBench: Meta Continual Learning Benchmark on Resource-Constrained Edge Devices |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2504.00174 |