We propose MicroT, a low-energy, multi-task adaptive model framework for resource-constrained MCUs. We divide the original model into a feature extractor and a classifier. The feature extractor is obtained through self-supervised knowledge distillation and further optimized into part and full models through model splitting and joint training. These models are then deployed on MCUs, with classifiers added and trained on local tasks, ultimately performing stage-decision for joint inference. In this process, the part model initially processes the sample, and if the confidence score falls below the set threshold, the full model will resume and continue the inference. We evaluate MicroT on two models, three datasets, and two MCU boards. Our experimental evaluation shows that MicroT effectively improves model performance and reduces energy consumption when dealing with multiple local tasks. Compared to the unoptimized feature extractor, MicroT can improve accuracy by up to 9.87%. On MCUs, compared to the standard full model inference, MicroT can save up to about 29.13% in energy consumption. MicroT also allows users to adaptively adjust the stage-decision ratio as needed, better balancing model performance and energy consumption. Under the standard stage-decision ratio configuration, MicroT can increase accuracy by 5.91% and save about 14.47% of energy consumption.

翻译：我们提出了MicroT，一种面向资源受限微控制器（MCU）的低能耗多任务自适应模型框架。我们将原始模型划分为特征提取器和分类器两部分。特征提取器通过自监督知识蒸馏获得，并进一步通过模型分割与联合训练优化为部分模型和完整模型。这些模型随后被部署在MCU上，通过添加分类器并在本地任务上进行训练，最终执行阶段决策以进行联合推理。在此过程中，部分模型首先处理样本，若置信度分数低于设定阈值，则完整模型将接续完成推理。我们在两种模型架构、三个数据集和两款MCU开发板上对MicroT进行了评估。实验结果表明，MicroT在处理多个本地任务时能有效提升模型性能并降低能耗。与未优化的特征提取器相比，MicroT最高可提升9.87%的准确率。在MCU上，相较于标准的完整模型推理，MicroT最高可节省约29.13%的能耗。MicroT还允许用户根据需求自适应调整阶段决策比例，从而更好地平衡模型性能与能耗。在标准阶段决策比例配置下，MicroT可提升5.91%的准确率并节省约14.47%的能耗。