A. James Clark School of Engineering
Permanent URI for this communityhttp://hdl.handle.net/1903/1654
The collections in this community comprise faculty research works, as well as graduate theses and dissertations.
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Item DESIGN SPACE EXPLORATION FOR SIGNAL PROCESSING SYSTEMS USING LIGHTWEIGHT DATAFLOW GRAPHS(2018) Li, Lin; Bhattacharyya, Shuvra S; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Digital signal processing (DSP) is widely used in many types of devices, including mobile phones, tablets, personal computers, and numerous forms of embedded systems. Implementation of modern DSP applications is very challenging in part due to the complex design spaces that are involved. These design spaces involve many kinds of configurable parameters associated with the signal processing algorithms that are used, as well as different ways of mapping the algorithms onto the targeted platforms. In this thesis, we develop new algorithms, software tools and design methodologies to systematically explore the complex design spaces that are involved in design and implementation of signal processing systems. To improve the efficiency of design space exploration, we develop and apply compact system level models, which are carefully formulated to concisely capture key properties of signal processing algorithms, target platforms, and algorithm-platform interactions. Throughout the thesis, we develop design methodologies and tools for integrating new compact system level models and design space exploration methods with lightweight dataflow (LWDF) techniques for design and implementation of signal processing systems. LWDF is a previously-introduced approach for integrating new forms of design space exploration and system-level optimization into design processes for DSP systems. LWDF provides a compact set of retargetable application programming interfaces (APIs) that facilitates the integration of dataflow-based models and methods. Dataflow provides an important formal foundation for advanced DSP system design, and the flexible support for dataflow in LWDF facilitates experimentation with and application of novel design methods that are founded in dataflow concepts. Our developed methodologies apply LWDF programming to facilitate their application to different types of platforms and their efficient integration with platform-based tools for hardware/software implementation. Additionally, we introduce novel extensions to LWDF to improve its utility for digital hardware design and adaptive signal processing implementation. To address the aforementioned challenges of design space exploration and system optimization, we present a systematic multiobjective optimization framework for dataflow-based architectures. This framework builds on the methodology of multiobjective evolutionary algorithms and derives key system parameters subject to time-varying and multidimensional constraints on system performance. We demonstrate the framework by applying LWDF techniques to develop a dataflow-based architecture that can be dynamically reconfigured to realize strategic configurations in the underlying parameter space based on changing operational requirements. Secondly, we apply Markov decision processes (MDPs) for design space exploration in adaptive embedded signal processing systems. We propose a framework, known as the Hierarchical MDP framework for Compact System-level Modeling (HMCSM), which embraces MDPs to enable autonomous adaptation of embedded signal processing under multidimensional constraints and optimization objectives. The framework integrates automated, MDP-based generation of optimal reconfiguration policies, dataflow-based application modeling, and implementation of embedded control software that carries out the generated reconfiguration policies. Third, we present a new methodology for design and implementation of signal processing systems that are targeted to system-on-chip (SoC) platforms. The methodology is centered on the use of LWDF concepts and methods for applying principles of dataflow design at different layers of abstraction. The development processes integrated in our approach are software implementation, hardware implementation, hardware-software co-design, and optimized application mapping. The proposed methodology facilitates development and integration of signal processing hardware and software modules that involve heterogeneous programming languages and platforms. Through three case studies involving complex applications, we demonstrate the effectiveness of the proposed contributions for compact system level design and design space exploration: a digital predistortion (DPD) system, a reconfigurable channelizer for wireless communication, and a deep neural network (DNN) for vehicle classification.Item Analysis and Mitigation of Electromagnetic Noise in Resonant Cavities and Apertures(2004-08-10) Li, Lin; Ramahi, Omar M; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The trend of low voltage in electronics circuits and boards makes them vulnerable to electromagnetic interference (EMI). Furthermore, higher speed (clock rate) leads to faster switching which increases the potential for higher radiation from circuits and boards. These inevitable trends collectively compromise the electromagnetic compatibility of electronic systems by increasing their electromagnetic susceptibility. In this work, radiation from enclosures and apertures is studies and characterized and radiation mitigation techniques are proposed. High-speed circuit radiation within an enclosure leads to cavity resonance that can have critical impact on other electronic components housed within the same enclosure. The amplified electric field in the enclosure can couple to critical circuits leading to either hard or soft failures. One measure to gauge the resonance of an enclosure is through the determination of S-parameters between certain ports connected to the enclosure. In this work, different numerical methods for efficient prediction of S-parameters are proposed and evaluated for their effectiveness and accuracy. Once an efficient procedure is established for calculating S-parameters, novel topological variations within the enclosure can be tested before manufacturing using accurate numerical prototyping. The proposed numerical S-parameters calculation algorithms are validated by comparison to laboratory measurements. Radiation from resonant apertures present in the walls of enclosures represents a second major source for radiation. In this work, a novel analysis of aperture radiation is presented based on the interpretation of the aperture as a transmission line. Once the transmission line analogy is established, a novel aperture resonance mitigation technique is proposed based on the use of material coating that mimics the behavior of matching loads that typically terminate transmission lines. The technique consists of adding resistive sheets in selected places in, or around the aperture. The effectiveness of the proposed method is demonstrated by first using numerical simulation of an aperture present in an infinite perfectly conducting sheet, and then by designing an experiment where the novel technique proposed here is tested on resonant apertures present in a metallic box. Both radiation measurements in an anechoic chamber and S-parameters measurements were conducted to test the validity of the proposed mitigation techniques.