With the advancement of heterogeneous computing technique, efficient model inferences designed for various domains have been successfully developed upon real-time embedded platforms. For those applications (e.g., drones and autonomous driving) demanding both highly parallel computation via Graphics Processing Units (GPUs) and strict timeliness constraints, effective scheduling of real-time activities upon heterogeneous systems remains challenging. With the intrinsic and intricate interferences among tasks (contending for the GPU resources) being considered, we first investigate two resource-cognizant utilization bounds for partitioned-EDF (Earliest Deadline First) scheduling under suspension-oblivious (i.e., busy-waiting) paradigm, and then explore their non-monotonicities. Based on the insights gained from the bounds, we further propose SP-RA-TMA (Spatial-Partitioning and Resource-Aware Task Mapping Algorithm) for periodic tasks executing upon CPU-GPU systems. Specifically, several blocking-oriented approaches for appropriate vGPU-to-core partition and feasible task-to-core mapping are introduced, in order to tighten the bound of blocking overheads for tasks and effectively alleviate the negative effects of GPU resource competitions for better schedulability of task set and balanced system workload. Finally, the synthetic and empirical experiment results demonstrate the practicability of SP-RA-TMA that can achieve a higher acceptance ratio (e.g., 80% more) compared to the existing partitioned/dynamic schemes.

As accurate face recognition (FR) models based on deep learning can be easily trained using face images from various social media platforms, this phenomenon has raised ever-increasing concerns regarding user privacy. To address this issue, we investigate a privacy protection scheme based on multifeature fusion tensor (PPS-MFFT). Different from previous studies using a single feature or simple combination of several features, for every face image, PPS-MFFT first constructs a multifeature fusion tensor through hierarchically exploiting the correlations and complementarity between deep-learning features and those handcrafted features for stronger robustness and transferability. Further, on the basis of such tensors, the target images are reasonably chosen to enhance the camouflage effects while maintaining the visual similarities for final perturbed images, which are generated by means of developing a new optimization model for better tradeoff between effectiveness and practicability. Finally, the measurement results validate that both higher protective efficacy (e.g., 16% more in misidentifying the original face images) and acceptable visual effects can be obtained by PPS-MFFT when compared to the existing methods, and thus demonstrate the generality and applicability of our scheme.

MRI brain images are always of low contrast, which makes it difficult to identify to which area the information at the boundary of brain images belongs. This can make the extraction of features at the boundary more challenging, since those features can be misleading as they might mix properties of different brain regions. Hence, to alleviate such a problem, image boundary detection plays a vital role in medical image segmentation, and brain segmentation in particular, as unclear boundaries can worsen brain segmentation results. Yet, given the low quality of brain images, boundary detection in the context of brain image segmentation remains challenging. Despite the research invested to improve boundary detection and brain segmentation, these two problems were addressed independently, i.e., little attention was paid to applying boundary detection to brain segmentation tasks. Therefore, in this paper, we propose a boundary detection-based model for brain image segmentation. To this end, we first design a boundary segmentation network for detecting and segmenting images brain tissues. Then, we design a boundary information module (BIM) to distinguish boundaries from the three different brain tissues. After that, we add a boundary attention gate (BAG) to the encoder output layers of our transformer to capture more informative local details. We evaluate our proposed model on two datasets of brain tissue images, including infant and adult brains. The extensive evaluation experiments of our model show better performance (a Dice Coefficient (DC) accuracy of up to [Formula: see text] compared to the state-of-the-art models) in detecting and segmenting brain tissue images.

Heterogeneous multicore processors have recently become de facto computing engines for state-of-the-art embedded applications. Nonetheless, very little research focuses on the scheduling of periodic (implicit-deadline) real-time tasks upon heterogeneous multicores under the requirements of task synchronization, which is stemmed from resource access conflicts and can greatly affect the schedulability of tasks. In view of partitioned Earliest Deadline First and Multiprocessor Stack Resource Policy, we first discuss the blocking-aware utilization bound for uniform heterogeneous multicores and then illustrate its non-monotonicity, where the bound may decrease with more deployed cores. Following the insights obtained from the bound analysis, taking the system heterogeneity into consideration, we propose a Synchronization-Aware Task Partitioning Algorithm for Heterogeneous Multicores (SA-TPA-HM)). Several resource-guided and heterogeneity-oriented mapping heuristics are incorporated to reduce the negative impacts of blocking interferences for better schedulability performance of tasks and balanced workload distribution across cores. The extensive simulation results show that SA-TPA-HM can obtain the schedulability ratios approximate to an Integer Non-Linear Programming--based solution, and much higher (e.g., 60% more) in contrast to the existing partitioning algorithms targeted at homogeneous multicores. The measurement results in Linux kernel further reveal the practical viability of SA-TPA-HM that can experience lower runtime overhead (e.g., 15% less) when compared to other mapping schemes.

As the computing devices and software executions are susceptible to manifold faults, fault tolerance has been an important research topic in safety-critical real-time systems. Moreover, multicore processors have recently emerged as prevailing computing engines for modern embedded systems. However, there exists rather rare work on the fault-tolerant scheduling of real-time tasks executing on multicores with shared resources, where the task synchronization originated from resource access contention may significantly degrade the schedulability of task system. With the focus on the partitioned-EDF scheduler with the MSRP (Multiprocessor Stack Resource Policy) protocol and primary/backup recovery mechanism, we first investigate a utilization bound and then identify its anomaly where the bound may decrease when more cores are deployed. Next, following the insights gained by the analysis of the bound, we propose a reliability and synchronization aware task partitioning algorithm (RSA-TPA) together with an efficient version to implement the joint management of task synchronization and system reliability, where several resource-oriented heuristics are developed to improve both the schedulability performance and workload balancing. The extensive simulation results show that the RSA-TPA schemes can obtain higher acceptance ratio (e.g., 60 percent more) and generate more balanced partitions, when compared to the existing schemes that consider either reliability management or task synchronization. Finally, with the different fault arrival rates being considered, the actual implementation in Linux kernel further demonstrates the applicability of RSA-TPA that has lower run-time overhead (e.g., 20 percent less) in comparison with other mapping algorithms.

With an objective to provide the proactive and personalized services for human beings, Cyber-Physical-Social Systems (CPSS), which combine the cyber space, physical space, and social space together, need to process the large scale heterogenous data first. Tensor, as an appropriate data representation tool, has been widely used for representation of heterogeneous Cyber-Physical-Social big data. When computationally processing such tensor, many necessary constraints have to be taken into account, e.g., the execution time, energy consumption, economic cost, security as well as reliability. However, the systematic integration of these constraints and then the modelling of general optimization for tensor processing become more challenging. In this paper, with such constraints being considered together, a general model for tensor computation that optimizes the execution time, energy consumption, and economic cost with acceptable security and reliability is proposed. From diverse perspectives of user requirements, a case study for the tree-based distributed High-Order Singular Value Decomposition (HOSVD) is measured. With the focus on multi-objective combination, the experimental results validate the applicability and generality of the proposed model.

Heterogeneous multicore processors have become popular computing engines for modern embedded real-time systems recently. However, there is rather limited research on the scheduling of real-time tasks running on heterogeneous multicore systems with shared resources. Note that, different partitionings of tasks upon heterogeneous cores can affect the synchronization overheads of tasks (and thus the system schedulability). Focusing on the partitioned-EDF scheduling and resource access protocol MSRP (Multiprocessor Stack Resource Policy), this paper proposes an effective synchronization aware task partitioning algorithm for heterogeneous multicores (SATPA-HM). Several resource-oriented heuristics are exploited to tighten the bound on the synchronization costs of tasks through dynamic task prioritization and to find an appropriate core for each task that can minimize the system utilization increment. The simulation results show that our proposed SA-TPA-HM scheme can achieve higher acceptance ratio (e.g., 60% more), when compared to the existing schemes designed for homogeneous multicores.

In mixed-criticality (MC) systems, multiple activities with various certification requirements (thus with different criticality levels) can co-exist on shared hardware platforms, where multicore processors have emerged as the de facto computing engines. In this paper, by using the partitioned earliest-deadline-first with virtual deadlines (EDF-VDs) scheduler for a set of periodic MC tasks running on multicore systems, we derive a criticality-aware utilization bound for efficient feasibility tests and then identify its characteristics. Our analysis shows that the bound increases with increasing number of cores and decreasing system criticality level. We show that, since the utilizations of MC tasks at different criticality levels can vary considerably, the utilization contribution of a task on different cores may have large variations and thus can significantly affect the system schedulability under the EDF-VD scheduler. Based on these observations, we propose a novel and efficient criticality-aware task partitioning algorithm (CA-TPA) to compensate for the inherent pessimism of the utilization bound. In order to improve the system schedulability, the task priorities are determined according to their utilization contributions to the system in CA-TPA. Moreover, by analyzing the utilization variations of tasks at different levels, we develop several heuristics to minimize the utilization increment and balance the workload on cores. The simulation results show that the CA-TPA scheme is very effective in achieving higher schedulability ratio and yielding balanced workloads. The actual implementation in Linux operating system further demonstrates the applicability of CA-TPA with lower run-time overhead, compared to the existing partitioning schemes.

In mixed-criticality (MC) system, diverse application activities with various certification requirements (different criticality) can share a computing platform, where multicore processors have emerged as the prevailing computing engines. Focusing on the problem of resource access contention in multicore MC systems, we analyze the synchronization issues and blocking characteristics of the Multiprocessor Stack Resource Policy (MSRP) with both priority and criticality inversions among MC tasks being considered. We develop the first criticality-aware utilization bound under partitioned Earliest Deadline First (EDF) and MSRP by taking the worst case synchronization overheads of tasks into account. The non-monotonicityof the bound where it may decrease when more cores are deployed is identified, which can cause anomalies in the feasibility tests. With the objective to improve system schedulability, a novel criticality-cognizant and resource-oriented analysis approach is further studied to tighten the bound on the synchronization overheads for MC tasks scheduled under partitioned EDF and MSRP. The simulation results show that the new analysis approach can effectively reduce the blocking times for tasks (up to 30 percent) and thus improve the schedulability ratio (e.g., 10 percent more). The actual implementation in Linux kernel further shows the practicability of partitioned-EDF with MSRP (with run-time overhead being about 3 to 7 percent of the overall execution time) for MC tasks running on multicores with shared resources.

The scheduling for mixed-criticality (MC) systems, where multiple activities have different certification requirements and thus different criticality on a shared hardware platform, has recently become an important research focus. In this work, considering that multicore processors have emerged as the de-facto platform for modern embedded systems, we propose a novel and efficient criticality-aware task partitioning algorithm (CA-TPA) for a set of periodic MC tasks running on multicore systems. We employ the state-of-the art EDF-VD scheduler on each core. Our work is based on the observation that the utilizations of MC tasks at different criticality levels can have quite large variations, hence when a task is allocated, its utilization contribution on different processors may vary by large margins and this can significantly affect the schedulability of tasks. During partitioning, CA-TPA sorts the tasks according to their utilization contributions on individual processors. Several heuristics are investigated to balance the workload on processors with the objective of improving the schedulability of tasks under CA-TPA. The simulation results show that our proposed CA-TPA scheme is effective, giving much higher schedulability ratios when compared to the classical partitioning schemes.

Network-on-Chip (NoC) has emerged as interconnect paradigm in state-of-the-art multi/many core architectures. Voltage and frequency island (VFI) was recently adopted as an effective energy management technique for large scale multicore chip designs. Focusing on NoCand VFI-based multi/many core real-time systems with Dynamic Voltage and Frequency Scaling (DVFS) capability, we study both static and dynamic contention-aware energy management schemes for task set with precedence relationships and a common deadline. First, our static schemes utilize two approaches with contention awareness to obtain the mapping of tasks to cores together with scheduling of communications on NoC for minimizing makespan, and thus can potentially lower uniform scaled frequency for cores and links while meeting the timeliness. Next, different from other existing schemes, by incorporating the latency due to network congestions into the analysis, our dynamic contention-aware energy management schemes perform the allocation of feasible slack to tasks and communications simultaneously for further energy savings, subject to common voltage and frequency limitations of VFI and timing constraints of task set. The results through extensive simulations and case studies show that, compared to heuristicbased and INLP-based task mapping solutions (with pessimistic estimation of communication contention), our static scheme can obtain better energy savings (e.g., 25 percent more). The results also show that our dynamic scheme can save up to 45 percent more energy compared to our static scheme under deadline guarantee, while the online scheme ignoring the traffic congestions in NoC can result in serious deadline violation and usually more energy consumption (e.g., 15 percent more).

In real-time systems, both scheduling theory and resource access protocols have been studied extensively. However, there is very limited research on scheduling algorithms for real-time systems with shared resources, where the problem becomes more prominent with the emergence of multicore processors. In this paper, focusing on partitioned-EDF scheduling and MSRP resource access protocol, we study the utilization bound and efficient task mapping schemes for a set of periodic real-time tasks that access shared resources in multiprocessor/multicore systems. Specifically, with synchronization overhead being considered, we illustrate the schedulability anomaly for such systems. We develop the first synchronization-cognizant utilization bound and further analyze its non-monotonicity where the bound can decrease when more processors are deployed. Then, we show that finding the optimal mapping for tasks with shared resources is NP-hard. Based on a novel approach that iteratively tightens the synchronization overhead, we propose two efficient synchronization-cognizant task mapping algorithms (SC-TMA) with the goal of achieving better schedulability and balanced workload on deployed processors. Finally, the proposed SC-TMA schemes are evaluated through extensive simulations with synthetic tasks. The results show that, the schedulability ratio and (average) system load under SC-TMA are close to that of an INLP (Integer Non-Linear Programming) based solution for small task systems. When compared to the existing task mapping algorithms, SC-TMA obtain much better schedulability ratio and lower/balanced workload on all processors.

Voltage and frequency island (VFI) was recently adopted as an effective energy management technique for multicore processors. For a set of periodic real-time tasks that access shared resources running on a VFI-based multicore system with dynamic voltage and frequency scaling (DVFS) capability, we study both static and dynamic synchronization-aware energy management schemes. First, based on the enhanced MSRP resource access protocol with a suspension mechanism, we devise a synchronization-aware task mapping heuristic for partitioned-EDF scheduling, which assigns tasks that access similar set of resources to the same core to reduce the synchronization overhead and thus improve schedulability. Then, static schemes that assign uniform and different scaled frequencies for tasks on different VFIs are studied. To further exploit dynamic slack, we propose an integrated synchronization-aware slack management framework to appropriately reclaim, preserve, release and steal slack at runtime to slow down the execution of tasks subject to the common voltage/frequency limitation of VFIs and timing/synchronization constraints of tasks. Taking the additional delay due to task synchronization into consideration, the new scheme allocates slack in a fair manner and scales down the execution of both noncritical and critical sections of tasks for more energy savings. Simulation results show that, the synchronization-aware mapping can significantly improve the schedulability of tasks. The energy savings obtained by the static scheme with different frequencies for tasks on different VFIs is close to that of an optimal Integer Nonlinear Programming (INLP) solution. Moreover, compared to the simple extension of existing solutions for uniprocessor systems, our schemes can obtain much better energy savings (up to 40 percent) with comparable DVFS overhead.

Many research efforts have been done in the domain of static scheduling algorithms based on DAG. However, most of these literatures assume that all processors are fully connected and receive communication data concurrently, while ignoring the contentions and delays on network links in real applications, which leads to low efficiency. This paper focuses on the issue of edge scheduling for dependent task set in parallel and distributed environment. Combined with conventionally efficient heuristics, two contention-aware scheduling algorithms are proposed in the paper: OIHSA (Optimal Insertion Hybrid Scheduling Algorithm) and BBSA (Bandwidth Based Scheduling Algorithm). Both the proposed algorithms start from the inherent characteristic of the edge scheduling problem, and select route paths with relatively low network workload to transfer communication data by modified routing algorithm. OISHA optimizes the start time of communication data transferred on links in form of theorems. BBSA exploits bandwidth of network links fully to transfer communication data as soon as possible. Therefore, the makespan yielded by our algorithms can be reduced efficiently. Moreover, the proposed algorithms adapt to not only homogeneous systems but also heterogeneous systems. The experiment results indicate that the proposed algorithms obviously outperform other algorithms so far in terms of makespan.

Summary form only given. Distributed computing environment composed of interconnected machines with varied or same computational capabilities is well suited to meet the computational demands of diverse groups of tasks. The most popular model characterizing tasks' dependence is to utilize DAG (directed acyclic graph). We present a novel model called TTIG that is more realistic and universal than DAG and its corresponding algorithm called MATE for static mapping of parallel application. We extend TTIG model, and propose a new static scheduling algorithm called GBHA (group-based hybrid algorithm) and two versions (GBHA1 for homogeneous systems and GBHA2 for heterogeneous systems). In this work, our algorithms are compared with MATE and some well-known scheduling algorithms for multiprocessor systems based on DAG The simulation experiment results show that our algorithms outperform MATE significantly in both homogeneous and heterogeneous systems and can be comparable to efficient scheduling algorithms based on DAG in multiprocessor systems but with much lower complexity.