| 1. |
- Adiththan, Arun, et al.
(författare)
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Cloud-assisted Control of Ground Vehicles using Adaptive Computation Offloading Techniques
- 2018
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Ingår i: PROCEEDINGS OF THE 2018 DESIGN, AUTOMATION and TEST IN EUROPE CONFERENCE and EXHIBITION (DATE). - : IEEE. - 9783981926309 ; , s. 589-592
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Konferensbidrag (refereegranskat)abstract
- The existing approaches to design efficient safety critical control applications is constrained by limited in-vehicle sensing and computational capabilities. In the context of automated driving, we argue that there is a need to leverage resources "out-of-the-vehicle" to meet the sensing and powerful processing requirements of sophisticated algorithms (e.g., deep neural networks). To realize the need, a suitable computation offloading technique that meets the vehicle safety and stability requirements, even in the presence of unreliable communication network, has to be identified. In this work, we propose an adaptive offloading technique for control computations into the cloud. The proposed approach considers both current network conditions and control application requirements to determine the feasibility of leveraging remote computation and storage resources. As a case study, we describe a cloud-based path following controller application that leverages crowdsensed data for path planning.
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| 2. |
- Aminifar, Amir, et al.
(författare)
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Control-Quality Driven Task Mapping for Distributed Embedded Control Systems
- 2011
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Ingår i: <em>Embedded and Real-Time Computing Systems and Applications (RTCSA), 2011 IEEE 17th International Conference on</em>. - : IEEE. - 9781457711183 ; , s. 133-142
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Konferensbidrag (refereegranskat)abstract
- Many embedded control systems are implemented on execution platforms with several computation nodes and communication components. Distributed embedded control systems typically comprise multiple control loops that share the available computation and communication resources of the platform. It is well known that such resource sharing leads to complex delay characteristics that degrade the control quality if not properly taken into account at design time. Scheduling in computation nodes and communication infrastructure, as well as execution periods of the controllers impact the delay characteristics and, consequently, the control quality. In addition, mapping of tasks on computation nodes affect both scheduling of tasks and messages, and the assignment of periods of the control applications. Therefore, control synthesis must be considered during mapping, scheduling, and period assignment in order to achieve high control quality. This paper presents a control-quality optimization approach for integrated mapping, scheduling, period selection, and control synthesis for distributed embedded control systems.
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| 3. |
- Aminifar, Amir, et al.
(författare)
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Designing High-Quality Embedded Control Systems with Guaranteed Stability
- 2012
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Ingår i: 33rd IEEE Real-Time Systems Symposium (RTSS 2012. - 1052-8725. - 9781467330985
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Konferensbidrag (refereegranskat)abstract
- Many embedded systems comprise several controllers sharing available resources. It is well known that such resource sharing leads to complex timing behavior that degrades the quality of control, and more importantly, can jeopardize stability in the worst-case, if not properly taken into account during design. Although stability of the control applications is absolutely essential, a design flow driven by the worst-case scenario often leads to poor control quality due to the significant amount of pessimism involved and the fact that the worst-case scenario occurs very rarely. On the other hand, designing the system merely based on control quality, determined by the expected (average-case) behavior, does not guarantee the stability of control applications in the worst-case. Therefore, both control quality and worst-case stability have to be considered during the design process, i.e., period assignment, task scheduling, and control-synthesis. In this paper, we present an integrated approach for designing high-quality embedded control systems, while guaranteeing their stability.
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| 4. |
- Baek, Iljoo, et al.
(författare)
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CARSS: Client-Aware Resource Sharing and Scheduling for Heterogeneous Applications
- 2020
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Konferensbidrag (refereegranskat)abstract
- Modern hardware accelerators such as GP-GPUs and DSPs are commonly being used in real-time settings such as high-performance multimedia systems and autonomous vehicles. In fact, the throughput of a wide variety of computationally demanding tasks from 3D graphics and rendering to image processing and deep learning can benefit from such specialized hardware. Such heterogeneity can affect the performance of applications running simultaneously on the same accelerator. Prior studies on resource sharing and scheduling on hardware accelerators have not attempted to account for this context. In this work, we provide a portable tagging-based cooperative scheduler and resource monitor for use by heterogeneous applications sharing a single hardware accelerator in a soft real-time environment. We also offer practical insight into how various types of applications use the hardware accelerators differently. We substantiate the feasibility of our approach and evaluate the improvement of various scheduling policies over a proprietary scheduler in several case-studies with real-world applications on 2 NVIDIA platforms: a GeForce GTX 1070 GPU and an Xavier embedded platform 1 . Although we focus on GPUs in this paper, our underlying observations and framework can also be used for sharing execution on other types of hardware accelerators.
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| 5. |
- Baek, Iljoo, et al.
(författare)
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FT-DeepNets: Fault-Tolerant Convolutional Neural Networks with Kernel-based Duplication
- 2022
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Ingår i: 2022 IEEE WINTER CONFERENCE ON APPLICATIONS OF COMPUTER VISION (WACV 2022). - : IEEE COMPUTER SOC. - 9781665409155 - 9781665409162 ; , s. 1878-1887
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Konferensbidrag (refereegranskat)abstract
- Deep neural network (deepnet) applications play a crucial role in safety-critical systems such as autonomous vehicles (AVs). An AV must drive safely towards its destination, avoiding obstacles, and respond quickly when the vehicle must stop. Any transient errors in software calculations or hardware memory in these deepnet applications can potentially lead to dramatically incorrect results. Therefore, assessing and mitigating any transient errors and providing robust results are important for safety-critical systems. Previous research on this subject focused on detecting errors and then recovering from the errors by re-running the network. Other approaches were based on the extent of full network duplication such as the ensemble learning-based approach to boost system fault-tolerance by leveraging each models advantages. However, it is hard to detect errors in a deep neural network, and the computational overhead of full redundancy can be substantial. We first study the impact of the error types and locations in deepnets. We next focus on selecting which part should be duplicated using multiple ranking methods to measure the order of importance among neurons. We find that the duplication overhead for computation and memory is a tradeoff between algorithmic performance and robustness. To achieve higher robustness with less system overhead, we present two error protection mechanisms that only duplicate parts of the network from critical neurons. Finally, we substantiate the practical feasibility of our approach and evaluate the improvement in the accuracy of a deepnet in the presence of errors. We demonstrate these results using a case study with real-world applications on an Nvidia GeForce RTX 2070Ti GPU and an Nvidia Xavier embedded platform used by automotive OEMs.
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| 6. |
- Bhat, Anand, et al.
(författare)
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Practical Task Allocation for Software Fault-Tolerance and Its Implementation in Embedded Automotive Systems
- 2017
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Konferensbidrag (refereegranskat)abstract
- Due to the advent of active safety features and automated driving capabilities, the complexity of embedded computing systems within automobiles continues to increase. Such advanced driver assistance systems (ADAS) are inherently safety-critical and must tolerate failures in any subsystem. However, fault-tolerance in safety-critical systems has been traditionally supported by hardware replication, which is prohibitively expensive in terms of cost, weight, and size for the automotive market. Recent work has studied the use of software-based fault-tolerance techniques that utilize task-level hot and cold standbys to tolerate fail-stop processor and task failures. The benefit of using standbys is maximal when a task and any of its standbys obey the placement constraint of not being co-located on the same processor. We propose a new heuristic based on a "tiered" placement constraint, and show that our heuristic produces a better task assignment that saves at least one processor up to 40% of the time relative to the best known heuristic to date. We then introduce a task allocation algorithm that, for the first time to our knowledge, leverages the run-time attributes of cold standbys. Our empirical study finds that our heuristic uses no more than one additional processor in most cases relative to an optimal allocation that we construct for evaluation purposes using a creative technique. We have designed and implemented our software fault-tolerance framework in AUTOSAR, an automotive industry standard. We use this implementation to provide an experimental evaluation of our task-level fault-tolerance features. Finally, we present an analysis of the worst-case behavior of our task recovery features.
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| 7. |
- Bhat, Anand, et al.
(författare)
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Practical Task Allocation for Software Fault-Tolerance and Its Implementation in Embedded Automotive Systems
- 2019
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Ingår i: Real-time systems. - : Springer Science and Business Media LLC. - 0922-6443 .- 1573-1383. ; , s. 889-924
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Tidskriftsartikel (refereegranskat)abstract
- Due to the advent of active safety features and automated driving capabilities, the complexity of embedded computing systems within automobiles continues to increase. Such advanced driver assistance systems (ADAS) are inherently safety-critical and must tolerate failures in any subsystem. However, fault-tolerance in safety-critical systems has been traditionally supported by hardware replication, which is prohibitively expensive in terms of cost, weight, and size for the automotive market. Recent work has studied the use of software-based fault-tolerance techniques that utilize task-level hot and cold standbys to tolerate fail-stop processor and task failures. The benefit of using standbys is maximal when a task and any of its standbys obey the placement constraint of not being co-located on the same processor. We propose a new heuristic based on a “tiered” placement constraint, and show that our heuristic produces a better task assignment that saves at least one processor up to 40% of the time relative to the best known heuristic to date. We then introduce a task allocation algorithm that, for the first time to our knowledge, leverages the run-time attributes of cold standbys. Our empirical study finds that our heuristic uses no more than one additional processor in most cases relative to an optimal allocation that we construct for evaluation purposes using a creative technique. We also extend our heuristic to support mixed-criticality systems which allow for overload operation. We have designed and implemented our software fault-tolerance framework in AUTOSAR, an automotive industry standard. We use this implementation to provide an experimental evaluation of our task-level fault-tolerance features. Finally, we present an analysis of the worst-case behavior of our task recovery features.
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| 8. |
- Bhat, Anand, et al.
(författare)
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Recovery Time Considerations in Real-Time Systems Employing Software Fault Tolerance
- 2018
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Ingår i: 30th Euromicro Conference on Real-Time Systems (ECRTS 2018)}. - 9783959770750
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Konferensbidrag (refereegranskat)abstract
- Safety-critical real-time systems like modern automobiles with advanced driving-assist features must employ redundancy for crucial software tasks to tolerate permanent crash faults. This redundancy can be achieved by using techniques like active replication or the primary-backup approach. In such systems, the recovery time which is the amount of time it takes for a redundant task to take over execution on the failure of a primary task becomes a very important design parameter. The recovery time for a given task depends on various factors like task allocation, primary and redundant task priorities, system load and the scheduling policy. Each task can also have a different recovery time requirement (RTR). For example, in automobiles with automated driving features, safety-critical tasks like perception and steering control have strict RTRs, whereas such requirements are more relaxed in the case of tasks like heating control and mission planning. In this paper, we analyze the recovery time for software tasks in a real-time system employing Rate-Monotonic Scheduling (RMS). We derive bounds on the recovery times for different redundant task options and propose techniques to determine the redundant-task type for a task to satisfy its RTR. We also address the fault-tolerant task allocation problem, with the additional constraint of satisfying the RTR of each task in the system. Given that the problem of assigning tasks to processors is a well-known NP-hard bin-packing problem we propose computationally-efficient heuristics to find a feasible allocation of tasks and their redundant copies. We also apply the simulated annealing method to the fault-tolerant task allocation problem with RTR constraints and compare against our heuristics.
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| 9. |
- Bordoloi, Unmesh, et al.
(författare)
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The Frame Packing Problem for CAN-FD
- 2014
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Ingår i: <em>Real-Time Systems Symposium (RTSS 2014), Rome, Italy, Dec. 2-5, 2014.</em>. - : IEEE Press. ; , s. 284-293
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Konferensbidrag (refereegranskat)abstract
- CAN with flexible data rate (CAN-FD) allows transmission of larger payloads compared to standard CAN. However, efficient utilization of CAN-FD bandwidth space calls for a systematic strategy. The challenge arises from the nature of the frame sizes stipulated by CAN-FD as well as the heterogeneity of the periods of the messages and the signals. In this paper, we formulate a frame packing problem for CAN-FD with the optimization objective of bandwidth utilization while meeting temporal constraints. As part of the solution, first, we propose a formula to compute the best-case and the worst-case transmission times of the CAN-FD frames. Thereafter, we propose a framework that solves the optimization problem in pseudo-polynomial time. Experiments show the gains achieved by our framework. The results also show that, when applied to standard CAN, our heuristic provides improved results over existing techniques.
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| 10. |
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