Skyloft is a general and highly efficient user-space scheduling framework. It leverages user-mode interrupt to deliver and process hardware timers directly in user space. This capability enables Skyloft to achieve μs-scale preemption. Skyloft offers a set of scheduling interfaces that supports different scheduling policies, including both preemptive and nonpreemptive ones. Operating as a user-space scheduling framework, Skyloft is compatible with Linux and integrates seamlessly with high-performance I/O frameworks like DPDK.

Sleep staging by highly trained specialists is laborious. Although automatic sleep staging with supervised learning methods has been implemented for almost a decade, it requires lots of manually annotated data. Self-supervised learning methods have recently been gaining attention. They can learn representations with unlabeled data, which alleviates the cost of labeling work. However, the problem is that these self-supervised sleep staging methods either require prior knowledge, as with frequency information, or they produce unsatisfactory results. Thus, we propose a deep sequential sleep network (DSSNet), a self-supervised framework that aims to perform multi-view representations based on contrastive learning. It utilizes a single-channel electroencephalogram but achieves competitive performance. We also explore the impact of different contrastive mechanisms on DSSNet performance. The results of the Sleep-EDF dataset prove that the consistency of negative samples is crucial for improving performance. We evaluate DSSNet on Sleep-EDF and ISRUC-Sleep and achieve accuracies of 80.0% and 71.4%.

Atrial Fibrillation (AF) is considered to strongly correlate with stroke. Deep Neural Networks (DNNs) improve the accuracy in real-time atrial fibrillation detection. However, the deployment of DNNs on embedded systems is challenging due to hardware resources. To reduce computation loads, we study the feasibility to eliminate the redundant information in the AF detection task by downsampling. It is compatible with kernel-level optimization, quantization optimization, and model compression methods. A state-of-the-art deep learning model is used to estimate the amount of AF detection information among different sampling rates. This work considers both fixed-length and variable-length time intervals of an Electrocardiograph (ECG) segment. Experiment results demonstrate that model performance can be retained perfectly in AF detection. Ablation study experiments demonstrate the robustness with downsampled signals. Using a large time interval, the AF detection accuracy with 60 Hz signals can be compared to that with 300 Hz signals. Our on-going work includes designing lightweight DNN models with downsampled signals, further exploring the robustness of downsampled signals and model compression.