Self adaptive reconfigurable system based on middleware cross layer adaptation model

Loukil, Kais; Amor, Nader Ben; Abid, Mohamed

doi:10.1109/ssd.2009.4956824

Cited by 8 publications

(5 citation statements)

References 22 publications

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“…To validate our approach, we should make use of dynamic applications presenting uncertainty information. In [16] and [17] two image processing applications are represented. First one is a visual system embedded in a mobile robot, while the other is 3D synthesis images.…”

Section: Resultsmentioning

confidence: 99%

Prediction performance method for dynamic task scheduling, case study: the OLLAF Architecture

Ktata

Ghaffari

Granado

et al. 2010

2010 5th International Design and Test Workshop

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Actual dynamic applications, executed on real-time systems, have the tendency to be built on dynamically reconfigurable hardware devices. These applications require high performance and flexibility towards user and environment needs. To perform these application requirements, efficient mechanisms to manage hardware device must exist. In this paper we target OLLAF as a dynamically reconfigurable architecture which is designed to support complex and flexible applications. In order to deal with all of the dynamic aspects of such systems, we describe a predictive scheduling allowing an early estimation of our application dynamicity. A vision system of a mobile robot and an application of 3D synthesis images were served to validate the presented scheduling approach.

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Section: Resultsmentioning

confidence: 99%

Prediction performance method for dynamic task scheduling, case study: the OLLAF Architecture

Ktata

Ghaffari

Granado

et al. 2010

2010 5th International Design and Test Workshop

Self Cite

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“…Depending on the workloads and user QoS requirements, the system selects a suitable variant by evaluating all the variants in terms of the needs of the application, available free hardware resources, power and bandwidth constraints etc. The authors of [134] present the use of a global and local manager in the system, which together help system adaptation at the application, OS and the hardware level.…”

Section: Analysis Of Observed Workload Management Methodsmentioning

confidence: 99%

A Decision-Making Method for Run-Time Structural Adaptation Of FPGA-Based SOCS to Variations in Workload, Power Budget, Die Temperature, and Hardware Resources

Sharma

2024

Preprint

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<p>Embedded systems for application domains like robotics, aerospace, defense, etc. nowadays are developed on System-on-Chip (SoC) platforms based on Field Programmable Gate Array (FPGA) devices to support computation-intensive multi-task multi-modal dynamic workloads common for these applications. These systems therefore face the challenge to sustain the performance of their dynamic workloads in presence of variations in power budget, die temperature, and/or occurrence of hardware faults. This work proposes Run-time Structural Adaptation (RTSA) as a mechanism for mitigating these factors. One of the major contributions of this work is creation of a run-time decision-making method, ‚ÄúExplorer‚Äù, to carry out RTSA for FPGA-based SoCs and mitigate variations in the internal and external factors simultaneously. Whenever there is a change in the system‚Äôs set of constraints, Explorer selects an appropriate variant of hardware processing circuit for each active task from a large design space to form a system conÔ¨Åguration that satisÔ¨Åes each task‚Äôs performance speciÔ¨Åcation and all other system constraints. To support the practical deployment of Explorer, this work presents a method to derive run-time power consumption and die temperature estimation models for any FPGA-based device. This novel methodology of model derivation based on the FPGA platform and application running on it is another contribution of this work. The model derivation methods are formulated after detailed experimental analysis of power consumption and thermal behaviors of recent FPGA devices. Explorer can evaluate potential system conÔ¨Ågurations using the derived models to select a suitable system conÔ¨Åguration for RTSA. Experimental implementation of Explorer on the Zynq XC7Z020 SoC shows worst case execution time of 130 us, which demonstrates its suitability for RTSA for most applications associated with multi-stream computation-intensive tasks. This work also proposes an approach for automating model derivation which helps systems self-derive their model coefficients during system production and re-derive them due to changes in system platform, application, and/or environmental conditions. This significantly reduces model derivation time while avoiding any human involvement. This work thus presents the methods and models necessary to enable real systems utilizing FPGA devices to carry out RTSA and sustain their dynamic workloads amid dynamic environmental and hardware resource constraints.</p>

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“…e idea of using different implementation versions of tasks for run-time adaptation is gaining consideration, and some research work can be seen in this direction. e use of task implementation variants is observed for systems supporting software tasks [62][63][64], hardware tasks [65][66][67], and those supporting both, software and hardware tasks [68]. However, these methods target adaptation to individual parameters and not multiobjective adaptation process.…”

Section: Literature Reviewmentioning

confidence: 99%

A Decision-Making Method Providing Sustainability to FPGA-Based SoCs by Run-Time Structural Adaptation to Mode of Operation, Power Budget, and Die Temperature Variations

Sharma

Kirischian

2021

International Journal of Reconfigurable Computing

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One of the growing areas of application of embedded systems in robotics, aerospace, military, etc. is autonomous mobile systems. Usually, such embedded systems have multitask multimodal workloads. These systems must sustain the required performance of their dynamic workloads in presence of varying power budget due to rechargeable power sources, varying die temperature due to varying workloads and/or external temperature, and varying hardware resources due to occurrence of hardware faults. This paper proposes a run-time decision-making method, called Decision Space Explorer, for FPGA-based Systems-on-Chip (SoCs) to support changing workload requirements while simultaneously mitigating unpredictable variations in power budget, die temperature, and hardware resource constraints. It is based on the concept of Run-Time Structural Adaptation (RTSA); whenever there is a change in a system’s set of constraints, Explorer selects a suitable hardware processing circuit for each active task at an appropriate operating frequency such that all the constraints are satisfied. Explorer has been experimentally deployed on the ARM Cortex-A9 core of Xilinx Zynq XC7Z020 SoC. Its worst-case decision-making time for different scenarios ranges from tens to hundreds of microseconds. Explorer is thus suitable for enabling RTSA in systems where specifications of multiple objectives must be maintained simultaneously, making them self-sustainable.

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Self adaptive reconfigurable system based on middleware cross layer adaptation model

Cited by 8 publications

References 22 publications

Prediction performance method for dynamic task scheduling, case study: the OLLAF Architecture

Prediction performance method for dynamic task scheduling, case study: the OLLAF Architecture

A Decision-Making Method for Run-Time Structural Adaptation Of FPGA-Based SOCS to Variations in Workload, Power Budget, Die Temperature, and Hardware Resources

A Decision-Making Method Providing Sustainability to FPGA-Based SoCs by Run-Time Structural Adaptation to Mode of Operation, Power Budget, and Die Temperature Variations

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