Theses and Dissertations
This collection includes both ASU Theses and Dissertations, submitted by graduate students, and the Barrett, Honors College theses submitted by undergraduate students.
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- Creators: Li, Baoxin
- Creators: Lim, Chai Yong
Description
The rapid growth of Internet-of-things (IoT) and artificial intelligence applications have called forth a new computing paradigm--edge computing. Edge computing applications, such as video surveillance, autonomous driving, and augmented reality, are highly computationally intensive and require real-time processing. Current edge systems are typically based on commodity general-purpose hardware such as Central Processing Units (CPUs) and Graphical Processing Units (GPUs) , which are mainly designed for large, non-time-sensitive jobs in the cloud and do not match the needs of the edge workloads. Also, these systems are usually power hungry and are not suitable for resource-constrained edge deployments. Such application-hardware mismatch calls forth a new computing backbone to support the high-bandwidth, low-latency, and energy-efficient requirements. Also, the new system should be able to support a variety of edge applications with different characteristics. This thesis addresses the above challenges by studying the use of Field Programmable Gate Array (FPGA) -based computing systems for accelerating the edge workloads, from three critical angles. First, it investigates the feasibility of FPGAs for edge computing, in comparison to conventional CPUs and GPUs. Second, it studies the acceleration of common algorithmic characteristics, identified as loop patterns, using FPGAs, and develops a benchmark tool for analyzing the performance of these patterns on different accelerators. Third, it designs a new edge computing platform using multiple clustered FPGAs to provide high-bandwidth and low-latency acceleration of convolutional neural networks (CNNs) widely used in edge applications. Finally, it studies the acceleration of the emerging neural networks, randomly-wired neural networks, on the multi-FPGA platform. The experimental results from this work show that the new generation of workloads requires rethinking the current edge-computing architecture. First, through the acceleration of common loops, it demonstrates that FPGAs can outperform GPUs in specific loops types up to 14 times. Second, it shows the linear scalability of multi-FPGA platforms in accelerating neural networks. Third, it demonstrates the superiority of the new scheduler to optimally place randomly-wired neural networks on multi-FPGA platforms with 81.1 times better throughput than the available scheduling mechanisms.
ContributorsBiookaghazadeh, Saman (Author) / Zhao, Ming (Thesis advisor) / Ren, Fengbo (Thesis advisor) / Li, Baoxin (Committee member) / Seo, Jae-Sun (Committee member) / Arizona State University (Publisher)
Created2021
Description
Portable devices often require multiple power management IC (PMIC) to power different sub-modules, Li-ion batteries are well suited for portable devices because of its small size, high energy density and long life cycle. Since Li-ion battery is the major power source for portable device, fast and high-efficiency battery charging solution has become a major requirement in portable device application.
In the first part of dissertation, a high performance Li-ion switching battery charger is proposed. Cascaded two loop (CTL) control architecture is used for seamless CC-CV transition, time based technique is utilized to minimize controller area and power consumption. Time domain controller is implemented by using voltage controlled oscillator (VCO) and voltage controlled delay line (VCDL). Several efficiency improvement techniques such as segmented power-FET, quasi-zero voltage switching (QZVS) and switching frequency reduction are proposed. The proposed switching battery charger is able to provide maximum 2 A charging current and has an peak efficiency of 93.3%. By configure the charger as boost converter, the charger is able to provide maximum 1.5 A charging current while achieving 96.3% peak efficiency.
The second part of dissertation presents a digital low dropout regulator (DLDO) for system on a chip (SoC) in portable devices application. The proposed DLDO achieve fast transient settling time, lower undershoot/overshoot and higher PSR performance compared to state of the art. By having a good PSR performance, the proposed DLDO is able to power mixed signal load. To achieve a fast load transient response, a load transient detector (LTD) enables boost mode operation of the digital PI controller. The boost mode operation achieves sub microsecond settling time, and reduces the settling time by 50% to 250 ns, undershoot/overshoot by 35% to 250 mV and 17% to 125 mV without compromising the system stability.
In the first part of dissertation, a high performance Li-ion switching battery charger is proposed. Cascaded two loop (CTL) control architecture is used for seamless CC-CV transition, time based technique is utilized to minimize controller area and power consumption. Time domain controller is implemented by using voltage controlled oscillator (VCO) and voltage controlled delay line (VCDL). Several efficiency improvement techniques such as segmented power-FET, quasi-zero voltage switching (QZVS) and switching frequency reduction are proposed. The proposed switching battery charger is able to provide maximum 2 A charging current and has an peak efficiency of 93.3%. By configure the charger as boost converter, the charger is able to provide maximum 1.5 A charging current while achieving 96.3% peak efficiency.
The second part of dissertation presents a digital low dropout regulator (DLDO) for system on a chip (SoC) in portable devices application. The proposed DLDO achieve fast transient settling time, lower undershoot/overshoot and higher PSR performance compared to state of the art. By having a good PSR performance, the proposed DLDO is able to power mixed signal load. To achieve a fast load transient response, a load transient detector (LTD) enables boost mode operation of the digital PI controller. The boost mode operation achieves sub microsecond settling time, and reduces the settling time by 50% to 250 ns, undershoot/overshoot by 35% to 250 mV and 17% to 125 mV without compromising the system stability.
ContributorsLim, Chai Yong (Author) / Kiaei, Sayfe (Thesis advisor) / Bakkaloglu, Bertan (Committee member) / Ogras, Umit Y. (Committee member) / Seo, Jae-Sun (Committee member) / Arizona State University (Publisher)
Created2018
Description
The Internet-of-Things (IoT) boosts the vast amount of streaming data. However, even considering the growth of the cloud computing infrastructure, IoT devices will generate two orders of magnitude more than the capacity that centralized data center servers can process or store. This trend inevitability calls for the need for offloading IoT data processing to a decentralized edge computing infrastructure. On the other hand, deep-learning-based applications gain great progress by taking advantage of heavy centralized computing resources for training large models to fit increasingly complicated tasks. Even though large-scale deep learning models perform well in terms of accuracy, their high computational complexity makes it impossible to offload them onto edge devices for real-time inference and timely response. To enable timely IoT services on edge devices, this dissertation addresses the challenge from two perspectives. On the hardware side, a new field-programmable gate array (FPGA)-based framework for binary neural network and an application-specific integrated circuit (ASIC) accelerator for natural scene text interpretation are proposed, with the awareness of the computing resources and power constraint on edge. On the algorithm side, this work presents both the methodology of building more compact models and finding better computation-accuracy trade-off for existing models.
ContributorsLi, Yixing (Author) / Ren, Fengbo (Thesis advisor) / Vrudhula, Sarma (Committee member) / Seo, Jae-Sun (Committee member) / Li, Baoxin (Committee member) / Arizona State University (Publisher)
Created2021