12 mJ per Class On-Device Online Few-Shot Class-Incremental Learning
<|reference_start|>12 mJ per Class On-Device Online Few-Shot Class-Incremental Learning: Few-Shot Class-Incremental Learning (FSCIL) enables machine learning systems to expand their inference capabilities to new classes using only a few labeled examples, without forgetting the previously learned classes. Classical backpropagation-based learning and its variants are often unsuitable for battery-powered, memory-constrained systems at the extreme edge. In this work, we introduce Online Few-Shot Class-Incremental Learning (O-FSCIL), based on a lightweight model consisting of a pretrained and metalearned feature extractor and an expandable explicit memory storing the class prototypes. The architecture is pretrained with a novel feature orthogonality regularization and metalearned with a multi-margin loss. For learning a new class, our approach extends the explicit memory with novel class prototypes, while the remaining architecture is kept frozen. This allows learning previously unseen classes based on only a few examples with one single pass (hence online). O-FSCIL obtains an average accuracy of 68.62% on the FSCIL CIFAR100 benchmark, achieving state-of-the-art results. Tailored for ultra-low-power platforms, we implement O-FSCIL on the 60 mW GAP9 microcontroller, demonstrating online learning capabilities within just 12 mJ per new class.<|reference_end|>
@article{wibowo202412,
    title={12 mJ per Class On-Device Online Few-Shot Class-Incremental Learning},
    author={Yoga Esa Wibowo, Cristian Cioflan, Thorir Mar Ingolfsson, Michael
  Hersche, Leo Zhao, Abbas Rahimi, Luca Benini},
    journal={arXiv preprint arXiv:2403.07851},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.07851},
    primaryClass={cs.LG cs.CV}
}