This paper presents an approach to optimize the quality of computer vision tasks in resource-constrained devices by using different execution versions of the same task. The execution versions are generated by dropping irrelevant contents of the input images or other contents that have marginal effect on the quality of the result. Our execution model is designed to support the edge computing paradigm, where the tasks can be executed remotely on edge nodes either to improve the quality or to reduce the workload of the local device. We also propose an algorithm that selects the suitable execution versions, which includes selecting the configuration and the location of the execution, in order to maximize the total quality of the tasks based on the available resources. The proposed approach provides reliable and adaptive task execution by using several execution versions with various performance and quality trade-offs. Therefore, it is very beneficial for systems with resource and timing constraints such as portable medical devices, surveillance video cameras, wearable systems, etc. The proposed algorithm is evaluated using different computer vision benchmarks.

In this paper, we present a middleware to save energy in mobile computing devices that offload tasks to a remote server in the cloud. Saving energy in these devices is very important to prolong the battery life and avoid overheating. The middleware uses an available nearby device called auxiliary server either as a surrogate for the remote one, or as a proxy to pass the data between the mobile device and the remote server. The main idea is to reduce the energy consumption of the communication with the remote server by using a high-speed or a low-power local connection with the auxiliary server instead. The paper also analyzes when it is beneficial to use the auxiliary server based on the response time from the remote server and the bandwidth of the remote connection. The proposed middleware is evaluated using different benchmarks, including commonly used applications in mobile devices, and simulations. Furthermore, it is compared to state-of-the art approaches in this area. The experiments show that The middleware is energy-efficient especially when the bandwidth of the remote communication is relatively low or the server is overloaded.

Embedded systems have limited resources, such as computation capabilities and battery life. The Dynamic Voltage and Frequency Scaling (DVFS) technique is used to save energy by running the processor of the embedded system at low voltage and frequency levels. However, this prolongs the execution time, which may cause potential deadline misses for real-time tasks. In this paper, we propose a general-purpose middleware to reduce the energy consumption in embedded systems without violating the real-time constraints. The algorithms in the middleware adopt the computation offloading concept to reduce the workload on the processor of the embedded system by sending the computation-intensive tasks to a powerful server. The algorithms are further combined with the DVFS technique to find the running frequency (or speed) such that the energy consumption is minimized and the real-time constraints are satisfied. The evaluation shows that our approach reduces the average energy consumption down to nearly 60%, compared to executing all the tasks locally at the maximum processor speed.

To enable reliable embedded systems, it is imperative to leverage the compiler and system software for joint optimization of functional correctness (i.e., vulnerability indexes) and timing correctness (i.e., deadline misses). This paper considers the optimization of the reliability-timing (RT) penalty, defined as a linear combination of the vulnerability and deadline misses. We propose a cross-layer approach to achieve reliable code generation and execution at compilation and system software layers for embedded systems. This is enabled by the concept of generating multiple versions for given application functions, with diverse performance and reliability tradeoffs, by exploiting different reliability-guided compilation options. As the execution time of a function is not fixed, the selection of the versions depends upon the execution behavior of the previous functions. Based on the reliability and execution time profiling of these versions, our reliability-driven system software decides the prioritization of the functions for determining their execution order and employs dynamic version selection to dynamically select a suitable version of a function. Specifically, our scheme builds a schedule table offline to optimize the RT penalty, and uses this table at run time to select suitable versions for the subsequent functions. A complex real-world application of “secure video and audio processing” composed of various functions is evaluated for reliable code generation and execution.

There are many timing unreliable computing components in modern computer systems, which are typically forbidden in hard real-time systems due to the timing uncertainty. In this paper, we propose a computation offloading mechanism to utilise these timing unreliable components in a hard real-time system, by providing local compensations. The key of the mechanism is to decide (1) how the unreliable components are utilized and (2) how to set the worst-case estimated response time. The local compensation has to start when the unreliable components do not deliver the results in the estimated response time. We propose a scheduling algorithm and its schedulability test to analyze the feasibility of the compensation mechanism. To validate the proposed mechanism, we perform a case study based on image-processing applications in a robot system and simulations. By adopting the timing unreliable components, the system can handle higher-quality images and with better performance.

The applications of the mobile devices are increasingly being improved. They include computation-intensive tasks, such as video and audio processing. However, the mobile devices have limited resources, which may make it difficult to finish these tasks in time. Computation offloading can be used to boost the capabilities of these resource-constrained devices, where the computation-intensive tasks are moved to a powerful remote processing unit. This paper considers the computation offloading problem for sporadic real-time tasks. The total bandwidth server (TBS) is adopted on the remote processing unit (the server side) for resource reservation. On the client side, a dynamic programming algorithm is proposed to determine the offloading decision of the tasks such that their schedule is feasible (i.e., all the tasks meet their deadlines). The algorithm is evaluated using a case study of surveillance system and synthesized benchmarks.

Computation offloading concept has been recently adopted to improve the performance of embedded systems by moving some computation-intensive tasks (partially or wholly) to a powerful remote server. In this paper, we consider a computation offloading problem for frame-based real-time tasks, in which all the tasks have the same arrival time and the same relative deadline/period, by adopting the total bandwidth server (TBS) as resource reservations in the server side (remote execution unit). We prove that the problem is N P-complete and propose two algorithms in this paper. The first algorithm is a greedy algorithm with low complexity and provides a quick heuristic approach to decide which tasks to be offloaded and how the tasks are scheduled. The maximum finishing time of the solution derived from the greedy algorithm is at most twice of the finishing time (make span, maximal on the client and on the server) of any schedule. The second algorithm is a dynamic programming approach, which builds a three-dimensional table and requires pseudo-polynomial time complexity, to make an optimal decision for computation offloading. The algorithms are evaluated with a case study of a surveillance system and synthesized benchmarks.

To enable reliable embedded systems, it is imperative to leverage the compiler and system software for joint optimization of functional correctness, i.e., vulnerability indexes, and timing correctness, i.e., the deadline misses. This paper considers the optimization of the Reliability-Timing (RT) penalty, defined as a linear combination of the vulnerability indexes (reliability penalties) and the deadline misses. We propose a multi-layer approach to achieve reliable code generation and execution at compilation and system software layers for embedded systems. This is enabled by the concept of generating multiple versions, for given application functions, with diverse performance and reliability tradeoffs, by exploiting different reliability-guided compilation options. Based on the reliability and execution time profiling of these versions, our reliability-driven system software employs dynamic version selections to dynamically select a suitable version of a function according to the execution behavior of the previous functions. Specifically, our scheme builds a schedule table offline to optimize the RT penalty, and uses this table at run time to select suitable versions for the subsequent functions properly. A complex real-world application of “secure video and audio processing” composed of various functions is evaluated for reliable code generation and execution. The reliability analysis and evaluation is performed on a reliability-aware processor simulator.