Empowering On-Device Model Adaptation with an Edge AI Inference Accelerator

Abstract:On-device model adaptation is essential to enable lifelong personalization on resource-constrained hardware, but compute, power, and memory limitations of such devices make end-to-end backpropagation impractical for modern deep neural networks. This work proposes a heterogeneous adaptation pipeline that repurposes a commercial edge AI inference accelerator, Hailo-8L, for frozen-backbone feature extraction during on-device training. The computational graph is partitioned so that the pre-trained backbone is quantized to INT8 and run on the accelerator, while only a lightweight FP32 classification head is fine-tuned on the host CPU, enabling frequent, energy-efficient in-field updates with most weights remaining fixed. Across multiple architectures and datasets, this pipeline achieves up to 15.4x faster wall-clock training time compared to a Raspberry Pi 5 CPU baseline, offers competitive throughput in favorable settings, and consistently reduces energy per sample. Post-training quantization restoration is shown to be crucial for preserving the quality of accelerator-generated features and mitigating accuracy loss in quantization-sensitive architectures. Overall, the results demonstrate a practical approach to efficient on-device adaptation using inference-oriented edge accelerators. The implementation is available at this https URL.

Accepted at the ITEM Workshop, held in conjunction with ECML-PKDD 2026. To appear in the ECML-PKDD joint post-workshop proceedings

Machine Learning (cs.LG); Hardware Architecture (cs.AR); Computer Vision and Pattern Recognition (cs.CV)

arXiv:2607.18101 [cs.LG]

(or arXiv:2607.18101v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2607.18101 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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