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MicroT，一种面向资源受限MCU的低能耗多任务自适应模型框架。该方法将原始模型划分为特征提取器与分类器两部分：特征提取器通过自监督知识蒸馏获得，并经由模型拆分与联合训练优化为部分模型与完整模型。这些模型随后部署至MCU，通过添加分类器并在本地任务上进行训练，最终实现分阶段决策的联合推理。在此过程中，部分模型首先处理样本，若置信度低于设定阈值，完整模型将接管并继续推理。我们在两种模型、三个数据集及两块MCU开发板上对MicroT进行评估。实验结果表明，MicroT能有效提升多本地任务场景下的模型性能并降低能耗。相较于未优化的特征提取器，MicroT最高可提升9.87%的准确率；在MCU上，相较于标准完整模型推理，MicroT最高可节省约29.13%的能耗。同时，MicroT允许用户根据需求自适应调整分阶段决策比例，更好地平衡模型性能与能耗。在标准分阶段决策比例配置下，MicroT可实现5.91%的准确率提升并节省约14.47%的能耗。