QScheduler: Adaptive Gradient Sampling for Zeroth-Order On-Device Training on INT8 NPUs

Abstract:Zeroth-Order (ZO) optimization enables On-Device Learning (ODL) on NPU-equipped microcontrollers by estimating gradients through forward passes alone, bypassing the need for backpropagation primitives and reducing memory requirements. The number of gradient samples q critically affects training: insufficient samples produce noisy gradients that plateau early, while excessive samples consume more computational resources. However, finding an optimal q typically requires costly hyperparameter searches. This work introduces QScheduler, an adaptive algorithm that adjusts q based on training progress, and provides the first proof-of-concept of INT8 quantized on-device training on the STM32N6's Neural-ART NPU. Experiments on EuroSAT and STL-10 show that QScheduler matches well-tuned fixed-q configurations for both ResNet18 and MobileNetV2, without requiring prior q hyperparameter optimization.

Machine Learning (cs.LG)

arXiv:2607.18802 [cs.LG]

(or arXiv:2607.18802v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2607.18802 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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