Anas Toma

Reliable code generation and execution on unreliable hardware under joint functional and timing reliability considerations

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.

Computation Offloading for Frame-Based Real-Time Tasks with Resource Reservation Servers

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.

Computation Offloading by Using Timing Unreliable Components in Real-Time Systems

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.

Computation offloading for real-time systems

Computation offloading has been adopted to improve the performance of embedded systems by offloading the computation of some tasks, especially computation-intensive tasks, to servers or clouds. This paper explores computation offloading for real-time embedded systems to decide which tasks should be offloaded to get the results in time. Such a problem is NP-complete even for frame-based real-time tasks with the same period and relative deadline. We develop a pseudo-polynomial-time algorithm for deriving feasible schedules, if they exist.

Cross-Layer Software Dependability on Unreliable Hardware

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.

Server resource reservations for computation offloading in real-time embedded systems

Mobile devices have become very popular nowadays. They are used nearly everywhere. They run complex applications where the multimedia data are heavily processed. For example, ubiquitous applications in smart phones and different surveillance tasks on mobile robots. However, most of these applications have real-time constraints, and the resources of the mobile devices are limited. So, it is challenging to finish such complex applications on these resource-constrained devices without violating the real-time constraints. One solution is to adopt the Computation Offloading concept by moving some computation-intensive tasks to a powerful server. In this paper, we use the total bandwidth server (TBS) as resource reservations in the server side, and propose two algorithms based on the computation offloading to decide which tasks to be offloaded and how they are scheduled, such that the utilization (i.e., bandwidth) required from the server is minimized. We consider frame-based real-time tasks, in which all the tasks have the same arrival time, relative deadline and period. The first algorithm is a greedy algorithm with low complexity based on a fast heuristic. The second one is a pseudo-polynomial-time algorithm based on dynamic programming. Finally, the algorithms are evaluated with a case study for surveillance system and synthesized benchmarks.

Computation offloading for sporadic real-time tasks

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.

Real-time Low SNR Signal Processing for Nanoparticle Analysis with Deep Neural Networks.

In this work, we improve several steps of our PLASMON ASSISTED MICROSCOPY OF NANO-SIZED OBJECTS (PAMONO) sensor data processing pipeline through application of deep neural networks. The PAMONObiosensor is a mobile nanoparticle sensor utilizing SURFACE PLASMON RESONANCE (SPR) imaging for quantification and analysis of nanoparticles in liquid or air samples. Characteristics of PAMONO sensor data are spatiotemporal blob-like structures with very low SIGNAL-TO-NOISE RATIO (SNR), which indicate particle bindings and can be automatically analyzed with image processing methods. We propose and evaluate deep neural network architectures for spatiotemporal detection, time-series analysis and classification. We compare them to traditional methods like frequency domain or polygon shape features classified by a Random Forest classifier. It is shown that the application of deep learning enables our data processing pipeline to automatically detect and quantify 80 nm polystyrene particles and pushes the limits in blob detection with very low SNRs below one. In addition, we present benchmarks and show that real-time processing is achievable on consumer level desktop GRAPHICS PROCESSING UNITs (GPUs).

An Energy-Efficient Middleware for Computation Offloading in Real-Time Embedded Systems

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.

An automatic intelligent system for diagnosis and confirmation of Johne's disease

Johnes disease is one of the most widespread bacterial diseases of domestic animals. It causes yearly losses of billions of dollars worldwide. In this paper an automatic intelligent computer-aided system is proposed for the diagnosis of Johnes disease, the system uses image analysis and computer vision techniques to extract features from two different microscopic images, then those features are classified using neural networks and K-nearest neighbour K-NN techniques to diagnose Johnes disease. The proposed system employs histopathological examination to extract 192 different texture features. The features are then reduced into only 8 features and classified using artificial neural networks ANN. The acid fast stain test is used to confirm the positive cases. The construction and testing of both models are carried out using a total of 294 microscopic images, 194 images for the histopathological examination test which produces an overall accuracy of 98.33%. The other 100 images are used for the acid fast stain test, and it achieves an accuracy of 96.97%.