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.

MicroT 是一种低能耗的多任务自适应模型框架，适用于资源有限的微控制器。通过自我监督的知识蒸馏和模型的分离与联合训练，将原始模型分解为特征提取器和分类器，然后在微控制器上部署和训练这些模型，以执行关联推理和阶段决策，有效提高模型性能并减少能耗。