Video super-resolution (VSR) has become even more important recently to provide high resolution (HR) contents for ultra high definition displays. While many deep learning based VSR methods have been proposed, most of them rely heavily on the accuracy of motion estimation and compensation. We introduce a fundamentally different framework for VSR in this paper. We propose a novel end-to-end deep neural network that generates dynamic upsampling filters and a residual image, which are computed depending on the local spatio-temporal neighborhood of each pixel to avoid explicit motion compensation. With our approach, an HR image is reconstructed directly from the input image using the dynamic upsampling filters, and the fine details are added through the computed residual. Our network with the help of a new data augmentation technique can generate much sharper HR videos with temporal consistency, compared with the previous methods. We also provide analysis of our network through extensive experiments to show how the network deals with motions implicitly.

Real-time applications of future IT will continue to drive the demand for performance scaling in devices ranging from sensors to servers. Parallel processing in the form of mul-ticore and manycore architectures will also continue to be the principal route to unleashing next generation performance capabilities. To fully exploit multicore processors, real-time applications are expected to provide a large degree of parallel-ism, where real-time tasks can utilize multiple cores at the same time. Guaranteeing real-time performance, while making efficient use of multicore resources, requires a scheduling method that offers both high schedulability and effective load balancing. Many existing real-time scheduling methods for multicore systems focus on schedulability or load balancing, but not both -- each coming at the expense of the other. In this work we develop an efficient scheduling algorithm that not only guarantees real-time performance but also demonstrates effective distribution of tasks across cores. Experimental re-sults show that our method significantly outperforms state-of-the-art approaches in terms of load balancing while still providing good schedulability. We also show the benefits with respect to energy reduction that result from balanced load.

Energy consumption is a critical issue in parallel and distributed embedded systems. We present a novel algorithm for energy efficient scheduling of Directed Acyclic Graph (DAG) based applications on Dynamic Voltage Scaling (DVS) enabled systems. Experimental results show that our algorithm provides near optimal solutions for energy minimization with considerably smaller computational time and memory requirements compared to an existing algorithm that provides near optimal solutions.

Static DVS (dynamic voltage scaling) algorithms for DAG (directed acyclic graph) execution use the estimated execution time which is, in practice, an upper bound on the actual execution time to guarantee that an application completes in a given deadline. Therefore, many tasks may complete earlier than expected during the actual execution. This allows that the extra available slack can be allocated to tasks that have not yet begun execution with the goal of reducing the total energy requirements while still meeting the deadline constraints. In this paper, we present novel dynamic algorithms for reallocating the slack to future tasks. Experimental results show that our algorithms are comparable to static algorithms applied at runtime in terms of energy minimization, but require considerably smaller computational time.