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Project
System Scenario-Based Identification, Prediction and Intelligent Calibration of Processing Elements
In the past decade, real-time embedded systems have become much more complex due to the introduction of a lot of new functionality in one application, and due to running multiple applications concurrently. This increases the dynamic nature of today's applications and systems, and tightens the requirements for their constraints in terms of deadlines and energy consumption.
Recently, system-scenario-based design methodology (SSBDM) has been proposed to deal with the dynamic nature of modern embedded systems and reduce the newly introduced complexity. SSBDM can be utilized by existing design methodologies to increase their efficiency. It is based on the concept of system scenarios, which group system behaviors that are similar from a multidimensional cost perspective - such as resource requirements, delay, and energy consumption - in such a way that the system can be configured to exploit this cost similarity. As a generic and systematic design-time/run-time methodology, SSBDM contains the mechanisms for predicting the current scenario at run-time. The techniques for switching between scenarios are also available. This design trajectory is augmented with a run-time calibration mechanism, which allows the system to learn on-the-fly during its execution, and to adapt itself to the current input stimuli, by extending the scenario set, changing the scenario definitions, and both the prediction and switching mechanisms.
However in the current SSBDM approach, the identification of the system scenarios either heavily relies on the designer's experience, or lacks sound mathematical description. This leads to the difficulty in assessing the rationality of the identified system scenarios. In addition, the existing scenario predictor heavily depend on the numerical values of control variables that determine the control flows of the application behavior. The excessive dependency limits the capability to deal with the various uncertainties underlying design process.
Against this background, my research focuses on improving scenario identification and scenario prediction further to make them more reasonable and accurate. Furthermore, we propose a systematic system scenario methodology to handle the resource managment issues for dynamic applications.
Recently, system-scenario-based design methodology (SSBDM) has been proposed to deal with the dynamic nature of modern embedded systems and reduce the newly introduced complexity. SSBDM can be utilized by existing design methodologies to increase their efficiency. It is based on the concept of system scenarios, which group system behaviors that are similar from a multidimensional cost perspective - such as resource requirements, delay, and energy consumption - in such a way that the system can be configured to exploit this cost similarity. As a generic and systematic design-time/run-time methodology, SSBDM contains the mechanisms for predicting the current scenario at run-time. The techniques for switching between scenarios are also available. This design trajectory is augmented with a run-time calibration mechanism, which allows the system to learn on-the-fly during its execution, and to adapt itself to the current input stimuli, by extending the scenario set, changing the scenario definitions, and both the prediction and switching mechanisms.
However in the current SSBDM approach, the identification of the system scenarios either heavily relies on the designer's experience, or lacks sound mathematical description. This leads to the difficulty in assessing the rationality of the identified system scenarios. In addition, the existing scenario predictor heavily depend on the numerical values of control variables that determine the control flows of the application behavior. The excessive dependency limits the capability to deal with the various uncertainties underlying design process.
Against this background, my research focuses on improving scenario identification and scenario prediction further to make them more reasonable and accurate. Furthermore, we propose a systematic system scenario methodology to handle the resource managment issues for dynamic applications.
Date:22 Jun 2009 → 1 Oct 2010
Keywords:SSBDM, Energy consumption
Disciplines:Other engineering and technology
Project type:PhD project