Near-Threshold Computing embodies an intriguing choice for mobile processors due to the promise of superior energy efficiency, extending the battery life of these devices while reducing the peak power draw. However, process, voltage, and temperature variations cause a significantly high failure rate of Level One cache cells in the near-threshold regime a stark contrast to designs in the super-threshold regime, where fault sites are rare. This thesis work shows that faulty cells in the near-threshold regime are highly clustered in certain regions of the cache. In addition, popular mobile benchmarks are studied to investigate the impact of run-time workloads on timing faults manifestation. A technique to mitigate the run-time faults is proposed. This scheme maps frequently used data to healthy cache regions by exploiting the application cache behaviors. The results show up to 78% gain in performance over two other state-of-the-art techniques.

Indiana University-Purdue University Indianapolis (IUPUI) / Gait analysis plays a major role in treatment of osteoarthritis, knee or hip replacements, and musculoskeletal diseases. It is extensively used for injury rehabilitation and physical therapy for issues like Hemiplegia and Diplegia. It also provides us with the information to detect various improper gaits such as Parkinson's disease, Hemiplegic and diplegic gaits. Though there are many wearable and non-wearable methods to detect the improper gate performance, they are usually not user friendly and have restrictions. Most existing devices and systems can detect the gait but are very limited with regards of diagnosing them. The proposed method uses two A201 Force sensing resistors, accelerometer, and gyroscope to detect the gait and send diagnosed information of the possibility of the specified improper gaits via Bluetooth wireless communication system to the user's hand-held device or the desktop. The data received from the sensors was analyzed by the custom made micro-controller and is sent to the desktop or mobile device via Bluetooth module. The peak pressure values during a gait cycle were recorded and were used to indicate if the walk cycle of a person is normal or it has any abnormality. Future work: A magnetometer can be added to get more accurate results. More improper gaits can be detected by using two PCBs, one under each foot. Data can be sent to cloud and saved for future comparisons.

Indiana University-Purdue University Indianapolis (IUPUI) / Recent ULSI (ultra large scale integration) technology emphasizes small size devices, featuring low power and high switching speed. Moore's law has been followed successfully in scaling down the silicon device in order to enhance the level of integration with high performances until conventional devices failed to cop up with further scaling due to limitations with ballistic effects, and challenges with accommodating dopant fluctuation, mobility degradation, among other device parameters. Recently, Graphene based devices o ered alternative approach, featuring small size and high performances. This includes high carrier mobility, high carrier density, high robustness, and high thermal conductivity. These unique characteristics made the Graphene devices attractive for high speed electronic architectures. In this research, Graphene devices were integrated into applications with analog, digital, and mixed signals based systems. Graphene devices were briefly explored in electronics applications since its first model developed by the University of Illinois, Champaign in 2013. This study emphasizes the validation of the model in various applications with analog, digital, and mixed signals. At the analog level, the model was used for voltage and power amplifiers; classes A, B, and AB. At the digital level, the device model was validated within the universal gates, adders, multipliers, subtractors, multiplexers, demultiplexers, encoders, and comparators. The study was also extended to include Graphene devices for serializers, the digital systems incorporated into the data structure storage. At the mixed signal level, the device model was validated for the DACs/ADCs. In all components, the features of the new devices were emphasized as compared with the existing silicon technology. The system functionality and dynamic performances were also elaborated. The study also covered the linearity characteristics of the devices within full input range operation. GNRFETs with a minimum channel length of 10nm and an input voltage 0.7V were considered in the study. An electronic design platform ADS (Advanced Design Systems) was used in the simulations. The power amplifiers showed noise figure as low as 0.064dbs for class A, and 0.32 dbs for class B, and 0.69 dbs for class AB power amplifiers. The design was stable and as high as 5.12 for class A, 1.02 for class B, and 1.014 for class AB. The stability factor was estimated at 2GHz operation. The harmonics were as low as -100 dbs for class A, -60 dbs for class B, and -50dbs for class AB, all simulated at 1GHz. The device was incorporated into ADC system, and as low as 24.5 micro Watt power consumption and 40 nsec rise time were observed. Likewise, the DAC showed low power consumption as of 4.51 micro Watt. The serializer showed as minimum power consumption of the order of 0.4mW. These results showed that these nanoscale devices have potential future for high-speed communication systems, medical devices, computer architecture and dynamic Nano electromechanical (NEMS) which provides ultra-level of integration, incorporating embedded and IoT devices supporting this technology. Results of analog and digital components showed superiority over other silicon transistor technologies in their ultra-low power consumption and high switching speed.

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This research investigates lower-power lower-voltage analog circuit techniques suitable for wireless sensor applications. Wireless sensors have been used in a wide range of applications and will become ubiquitous with the revolution of internet of things (IoT). Due to the demand of low cost, miniature desirable size and long operating cycle, passive wireless sensors which don't require battery are more preferred. Such sensors harvest energy from energy sources in the environment such as radio frequency (RF) waves, vibration, thermal sources, etc. As a result, the obtained energy is very limited. This creates strong demand for low power, lower voltage circuits. The RF and analog circuits in the wireless sensor usually consume most of the power. This motivates the research presented in the dissertation. Specially, the research focuses on the design of a low power high efficiency regulator, low power Resistance to Digital Converter (RDC), low power Successive Approximation Register (SAR) Analog to Digital Converter (ADC) with parasitic error reduction and a low power low voltage Low Dropout (LDO) regulator. This dissertation includes a low power analog circuit design for the RFID wireless sensor which consists of the energy harvest circuits (an optimized rectifier and a regulator with high current efficiency) and a sensor measurement circuit (RDC), a single end sampling SAR ADC with no error induced by the parasitic capacitance and a digital loop LDO whose line and load variation response is improved. These techniques will boost the design of the wireless sensor and they can also be used in other similar low power design.

An important challenge in mobile sensor networks is to enable energy-efficient communication over a diversity of distances while being robust to wireless effects caused by node mobility. In this thesis, we argue that the pairing of two complementary radios with heterogeneous range characteristics enables greater range and interference diversity at lower energy cost than a single radio. We make three contributions towards the design of such multi-radio mobile sensor systems. First, we present the design of a novel reinforcement learning-based link layer algorithm that continually learns channel characteristics and dynamically decides when to switch between radios. Second, we describe a simple protocol that translates the benefits of the adaptive link layer into practice in an energy-efficient manner. Third, we present the design of Arthropod, a mote-class sensor platform that combines two such heterogeneous radios (XE1205 and CC2420) and our implementation of the Q-learning based switching protocol in TinyOS 2.0. Using experiments conducted in a variety of urban and forested environments, we show that our system achieves up to 52% energy gains over a single radio system while handling node mobility. Our results also show that our system can handle short, medium and long-term wireless interference in such environments.

Introduction: This paper presented an analysis of the energy consumption and responsiveness of MicroPython in an embedded system. The purpose of this study was to understand the energy consumption and response time of a MicroPython based system to optimize its overall performance and efficiency. Two research questions had been formulated to concretize the purpose of this thesis: [RQ1] How does the energy consumption of a MicroPython based embedded system compare to that of a C-based embedded system for tasks utilizing low power modes? [RQ2] What is the wake-up response time of MicroPython for low power modes when receiving external and internal interrupts, and how does it compare to an established language like C on an embedded system? Method: To answer the research questions and achieve the purpose, an experimental study was conducted. The energy consumption of the MicroPython based system was analyzed under different scenarios. The time it took for MicroPython to respond to an interrupt request from a sleeping state was also measured. The data collected from the experiment was analyzed to determine the level of energy consumption and responsiveness of MicroPython in an embedded system. Results: The results indicated that C was generally more energy efficient and responsive than MicroPython for tasks utilizing low power modes for the Deepsleep mode. Although MicroPython proved to have shorter response times for the Lightsleep low power mode. For energy consumption, C was more stable in the measurements while MicroPython reached both lower minimum currents and higher maximum currents. Conclusions: In conclusion, this study found that while MicroPython could achieve lower power levels than C in both low power modes tested, it reached higher current levels upon waking up. Despite this, MicroPython could still be a choice for applications that spend longer durations in low power modes, as this could offset the increased current spikes during wake-up. Response times for MicroPython were faster than C in the Lightsleep internal interrupt case, but MicroPython exhibited significantly longer response times in the Deepsleep mode due to the system resetting and restarting the interpreter. Keywords: Embedded systems, Energy consumption, Interrupt requests, Low power modes, MicroPython, Responsiveness.

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