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- All Subjects: Computer Engineering
- Genre: Doctoral Dissertation
Description
Multicore processors have proliferated in nearly all forms of computing, from servers, desktop, to smartphones. The primary reason for this large adoption of multicore processors is due to its ability to overcome the power-wall by providing higher performance at a lower power consumption rate. With multi-cores, there is increased need for dynamic energy management (DEM), much more than for single-core processors, as DEM for multi-cores is no more a mechanism just to ensure that a processor is kept under specified temperature limits, but also a set of techniques that manage various processor controls like dynamic voltage and frequency scaling (DVFS), task migration, fan speed, etc. to achieve a stated objective. The objectives span a wide range from maximizing throughput, minimizing power consumption, reducing peak temperature, maximizing energy efficiency, maximizing processor reliability, and so on, along with much more wider constraints of temperature, power, timing, and reliability constraints. Thus DEM can be very complex and challenging to achieve. Since often times many DEMs operate together on a single processor, there is a need to unify various DEM techniques. This dissertation address such a need. In this work, a framework for DEM is proposed that provides a unifying processor model that includes processor power, thermal, timing, and reliability models, supports various DEM control mechanisms, many different objective functions along with equally diverse constraint specifications. Using the framework, a range of novel solutions is derived for instances of DEM problems, that include maximizing processor performance, energy efficiency, or minimizing power consumption, peak temperature under constraints of maximum temperature, memory reliability and task deadlines. Finally, a robust closed-loop controller to implement the above solutions on a real processor platform with a very low operational overhead is proposed. Along with the controller design, a model identification methodology for obtaining the required power and thermal models for the controller is also discussed. The controller is architecture independent and hence easily portable across many platforms. The controller has been successfully deployed on Intel Sandy Bridge processor and the use of the controller has increased the energy efficiency of the processor by over 30%
ContributorsHanumaiah, Vinay (Author) / Vrudhula, Sarma (Thesis advisor) / Chatha, Karamvir (Committee member) / Chakrabarti, Chaitali (Committee member) / Rodriguez, Armando (Committee member) / Askin, Ronald (Committee member) / Arizona State University (Publisher)
Created2013
Description
One of the main challenges in testing artificial intelligence (AI) enabled cyber physicalsystems (CPS) such as autonomous driving systems and internet-of-things (IoT) medical
devices is the presence of machine learning components, for which formal properties are
difficult to establish. In addition, operational components interaction circumstances, inclusion of human-in-the-loop, and environmental changes result in a myriad of safety concerns
all of which may not only be comprehensibly tested before deployment but also may not
even have been detected during design and testing phase. This dissertation identifies major challenges of safety verification of AI-enabled safety critical systems and addresses the
safety problem by proposing an operational safety verification technique which relies on
solving the following subproblems:
1. Given Input/Output operational traces collected from sensors/actuators, automatically
learn a hybrid automata (HA) representation of the AI-enabled CPS.
2. Given the learned HA, evaluate the operational safety of AI-enabled CPS in the field.
This dissertation presents novel approaches for learning hybrid automata model from time
series traces collected from the operation of the AI-enabled CPS in the real world for linear
and nonlinear CPS. The learned model allows operational safety to be stringently evaluated
by comparing the learned HA model against a reference specifications model of the system.
The proposed techniques are evaluated on the artificial pancreas control system
devices is the presence of machine learning components, for which formal properties are
difficult to establish. In addition, operational components interaction circumstances, inclusion of human-in-the-loop, and environmental changes result in a myriad of safety concerns
all of which may not only be comprehensibly tested before deployment but also may not
even have been detected during design and testing phase. This dissertation identifies major challenges of safety verification of AI-enabled safety critical systems and addresses the
safety problem by proposing an operational safety verification technique which relies on
solving the following subproblems:
1. Given Input/Output operational traces collected from sensors/actuators, automatically
learn a hybrid automata (HA) representation of the AI-enabled CPS.
2. Given the learned HA, evaluate the operational safety of AI-enabled CPS in the field.
This dissertation presents novel approaches for learning hybrid automata model from time
series traces collected from the operation of the AI-enabled CPS in the real world for linear
and nonlinear CPS. The learned model allows operational safety to be stringently evaluated
by comparing the learned HA model against a reference specifications model of the system.
The proposed techniques are evaluated on the artificial pancreas control system
ContributorsLamrani, Imane (Author) / Gupta, Sandeep Ks (Thesis advisor) / Banerjee, Ayan (Committee member) / Zhang, Yi (Committee member) / Runger, George C. (Committee member) / Rodriguez, Armando (Committee member) / Arizona State University (Publisher)
Created2020