Matching Items (3)
Description
Mobile platforms are becoming highly heterogeneous by combining a powerful multiprocessor system-on-chip (MpSoC) with numerous resources including display, memory, power management IC (PMIC), battery and wireless modems into a compact package. Furthermore, the MpSoC itself is a heterogeneous resource that integrates many processing elements such as CPU cores, GPU, video, image, and audio processors. As a result, optimization approaches targeting mobile computing needs to consider the platform at various levels of granularity.
Platform energy consumption and responsiveness are two major considerations for mobile systems since they determine the battery life and user satisfaction, respectively. In this work, the models for power consumption, response time, and energy consumption of heterogeneous mobile platforms are presented. Then, these models are used to optimize the energy consumption of baseline platforms under power, response time, and temperature constraints with and without introducing new resources. It is shown, the optimal design choices depend on dynamic power management algorithm, and adding new resources is more energy efficient than scaling existing resources alone. The framework is verified through actual experiments on Qualcomm Snapdragon 800 based tablet MDP/T. Furthermore, usage of the framework at both design and runtime optimization is also presented.
Platform energy consumption and responsiveness are two major considerations for mobile systems since they determine the battery life and user satisfaction, respectively. In this work, the models for power consumption, response time, and energy consumption of heterogeneous mobile platforms are presented. Then, these models are used to optimize the energy consumption of baseline platforms under power, response time, and temperature constraints with and without introducing new resources. It is shown, the optimal design choices depend on dynamic power management algorithm, and adding new resources is more energy efficient than scaling existing resources alone. The framework is verified through actual experiments on Qualcomm Snapdragon 800 based tablet MDP/T. Furthermore, usage of the framework at both design and runtime optimization is also presented.
ContributorsGupta, Ujjwala (Author) / Ogras, Umit Y. (Thesis advisor) / Ozev, Sule (Committee member) / Chakrabarti, Chaitali (Committee member) / Arizona State University (Publisher)
Created2014
Description
Testing and calibration constitute a significant part of the overall manufacturing cost of microelectromechanical system (MEMS) devices. Developing a low-cost testing and calibration scheme applicable at the user side that ensures the continuous reliability and accuracy is a crucial need. The main purpose of testing is to eliminate defective devices and to verify the qualifications of a product is met. The calibration process for capacitive MEMS devices, for the most part, entails the determination of the mechanical sensitivity. In this work, a physical-stimulus-free built-in-self-test (BIST) integrated circuit (IC) design characterizing the sensitivity of capacitive MEMS accelerometers is presented. The BIST circuity can extract the amplitude and phase response of the acceleration sensor's mechanics under electrical excitation within 0.55% of error with respect to its mechanical sensitivity under the physical stimulus. Sensitivity characterization is performed using a low computation complexity multivariate linear regression model. The BIST circuitry maximizes the use of existing analog and mixed-signal readout signal chain and the host processor core, without the need for computationally expensive Fast Fourier Transform (FFT)-based approaches. The BIST IC is designed and fabricated using the 0.18-µm CMOS technology. The sensor analog front-end and BIST circuitry are integrated with a three-axis, low-g capacitive MEMS accelerometer in a single hermetically sealed package. The BIST circuitry occupies 0.3 mm2 with a total readout IC area of 1.0 mm2 and consumes 8.9 mW during self-test operation.
ContributorsOzel, Muhlis Kenan (Author) / Bakkaloglu, Bertan (Thesis advisor) / Ozev, Sule (Thesis advisor) / Kiaei, Sayfe (Committee member) / Ogras, Umit Y. (Committee member) / Arizona State University (Publisher)
Created2017
Description
The manufacturing process for electronic systems involves many players, from chip/board design and fabrication to firmware design and installation.
In today's global supply chain, any of these steps are prone to interference from rogue players, creating a security risk.
Manufactured devices need to be verified to perform only their intended operations since it is not economically feasible to control the supply chain and use only trusted facilities.
It is becoming increasingly necessary to trust but verify the received devices both at production and in the field.
Unauthorized hardware or firmware modifications, known as Trojans,
can steal information, drain the battery, or damage battery-driven embedded systems and lightweight Internet of Things (IoT) devices.
Since Trojans may be triggered in the field at an unknown instance,
it is essential to detect their presence at run-time.
However, it isn't easy to run sophisticated detection algorithms on these devices
due to limited computational power and energy, and in some cases, lack of accessibility.
Since finding a trusted sample is infeasible in general, the proposed technique is based on self-referencing to remove any effect of environmental or device-to-device variations in the frequency domain.
In particular, the self-referencing is achieved by exploiting the band-limited nature of Trojan activity using signal detection theory.
When the device enters the test mode, a predefined test application is run on the device
repetitively for a known period. The periodicity ensures that the spectral electromagnetic power of the test application concentrates at known frequencies, leaving the remaining frequencies within the operating bandwidth at the noise level. Any deviations from the noise level for these unoccupied frequency locations indicate the presence of unknown (unauthorized) activity. Hence, the malicious activity can differentiate without using a golden reference or any knowledge of the Trojan activity attributes.
The proposed technique's effectiveness is demonstrated through experiments with collecting and processing side-channel signals, such as involuntarily electromagnetic emissions and power consumption, of a wearable electronics prototype and commercial system-on-chip under a variety of practical scenarios.
In today's global supply chain, any of these steps are prone to interference from rogue players, creating a security risk.
Manufactured devices need to be verified to perform only their intended operations since it is not economically feasible to control the supply chain and use only trusted facilities.
It is becoming increasingly necessary to trust but verify the received devices both at production and in the field.
Unauthorized hardware or firmware modifications, known as Trojans,
can steal information, drain the battery, or damage battery-driven embedded systems and lightweight Internet of Things (IoT) devices.
Since Trojans may be triggered in the field at an unknown instance,
it is essential to detect their presence at run-time.
However, it isn't easy to run sophisticated detection algorithms on these devices
due to limited computational power and energy, and in some cases, lack of accessibility.
Since finding a trusted sample is infeasible in general, the proposed technique is based on self-referencing to remove any effect of environmental or device-to-device variations in the frequency domain.
In particular, the self-referencing is achieved by exploiting the band-limited nature of Trojan activity using signal detection theory.
When the device enters the test mode, a predefined test application is run on the device
repetitively for a known period. The periodicity ensures that the spectral electromagnetic power of the test application concentrates at known frequencies, leaving the remaining frequencies within the operating bandwidth at the noise level. Any deviations from the noise level for these unoccupied frequency locations indicate the presence of unknown (unauthorized) activity. Hence, the malicious activity can differentiate without using a golden reference or any knowledge of the Trojan activity attributes.
The proposed technique's effectiveness is demonstrated through experiments with collecting and processing side-channel signals, such as involuntarily electromagnetic emissions and power consumption, of a wearable electronics prototype and commercial system-on-chip under a variety of practical scenarios.
ContributorsKarabacak, Fatih (Author) / Ozev, Sule (Thesis advisor) / Ogras, Umit Y. (Thesis advisor) / Christen, Jennifer Blain (Committee member) / Kitchen, Jennifer (Committee member) / Arizona State University (Publisher)
Created2020