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DTSTART;TZID=America/Los_Angeles:20240524T093000
DTEND;TZID=America/Los_Angeles:20240524T103000
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CREATED:20240528T213210Z
LAST-MODIFIED:20240528T213610Z
UID:16300-1716543000-1716546600@www.cecs.uci.edu
SUMMARY:Deep Latent Variable Modeling of Physiological Signals
DESCRIPTION:Name: Khuong An Vo \nChair: Prof. Nikil Dutt \nDate: May 24\, 2024 \nTime:  9:30 AM \nLocation: Zoom \nCommittee: Prof. Erik Sudderth\, Prof. Hung Cao\, and Prof. Ramesh Srinivasan \nTitle: Deep Latent Variable Modeling of Physiological Signals \nAbstract: A deep latent variable model is a powerful tool for modeling complex distributions. These models assume that underlying structures\, but unobserved\, are present within the data. In this dissertation\, we explore high-dimensional problems related to physiological monitoring using latent variable models. First\, we present a novel deep state-space model to generate electrical waveforms of the heart using optically obtained signals as inputs. This can bring about clinical diagnoses of heart disease via simple assessment through wearable devices. Second\, we present a brain signal modeling scheme that combines the strengths of probabilistic graphical models and deep adversarial learning. The structured representations can provide interpretability and encode inductive biases to reduce the data complexity of neural oscillations. The efficacy of the learned representations is further studied in epilepsy seizure detection formulated as an unsupervised learning problem. Third\, we propose a framework for the joint modeling of physiological measures and behavior. Existing methods to combine multiple sources of brain data provided are limited. Direct analysis of the relationship between different types of physiological measures usually does not involve behavioral data. Our method can identify the unique and shared contributions of brain regions to behavior and can be used to discover new functions of brain regions. The success of these innovative computational methods would allow the translation of biomarker findings across species and provide insight into neurocognitive analysis in numerous biological studies and clinical diagnoses\, as well as emerging consumer applications. \nShort Bio: Khuong An Vo is a Ph.D. candidate in Computer Science at UC Irvine\, advised by Prof. Ramesh Srinivasan\, Prof. Nikil Dutt\, and Prof. Hung Cao. Prior\, he received a B.Eng. (Honors) degree in CS from HCMC Vietnam National University with research revolved around statistical natural language processing and has been applied in large-scale commercial systems. Now\, Vo’s research focuses on deep latent variable models in biomedical signal processing and neurocognitive modeling. He is also interested in the application of efficient deep learning in healthcare IoT. Vos’ work is supported by National Science Foundation (NSF) and National Institutes of Health (NIH) grants.
URL:https://www.cecs.uci.edu/event/deep-latent-variable-modeling-of-physiological-signals/
LOCATION:Zoom
CATEGORIES:PhD Defenses
ATTACH;FMTTYPE=image/jpeg:https://cecs.uci.edu/files/2024/05/Khuong-An-Vo-1.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20231130T093000
DTEND;TZID=America/Los_Angeles:20231130T103000
DTSTAMP:20240104T190401Z
CREATED:20240104T185923Z
LAST-MODIFIED:20240104T190401Z
UID:15937-1701336600-1701340200@www.cecs.uci.edu
SUMMARY:Towards Finding the Optimization Potential of Fine-Grained Locks
DESCRIPTION:Name:  Ahmad Nahian \nChair: Professor Brian Demsky \nDate: November 30\, 2023 \nTime:  9:30 AM \nLocation: ISEB 1010 \nCommittee: Professor Rainer Doemer\, Professor Quoc-Viet Dang \nTitle: Towards Finding the Optimization Potential of Fine-Grained Locks \nAbstract: Fine-grained locking is an effective way of eliminating lock contention in multi-threaded applications. However\, it is comparatively difficult and error prone to implement. To help programmers know the optimization potential of implementing fine-grained locking we have developed an approach that overcomes several shortcomings of prior approaches like record/replay. Our approach executes the existing implementation of the multi-threaded program with a custom thread scheduler that schedules threads according to their logical time\, which is the time it would have taken to execute the thread without waiting for unnecessary lock contention. The custom thread scheduler leverages a prediction mechanism to predict conflict relations among critical sections so that it schedules threads to generate the execution of an implementation with optimal fine-grained locking. We have implemented our approach in FGLPerf and tested it on several multi-threaded benchmarks. \nShort Bio: Ahmad Nahian is a PhD candidate in the EECS Computer Engineering program at UC Irvine\, working under the supervision of Professor Brian Demsky. Ahmad Nahian attained his BS in Computer Science and Engineering from Bangladesh University of Engineering and Technology and MS in Computer Engineering from UC Irvine. A notable work of his research is information flow profiler that finds optimization opportunities in multi-threaded programs. The information flow profiler is a powerful tool in that it can find new optimization opportunities along with optimization insights to eliminate synchronization bottlenecks that cannot be discovered by other state of the art profilers.
URL:https://www.cecs.uci.edu/event/towards-finding-the-optimization-potential-of-fine-grained-locks/
LOCATION:ISEB 1010\, Interdisciplinary Science and Engineering Building\, University of California\, Irvie\, CA\, 92697\, United States
CATEGORIES:PhD Defenses
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230706T090000
DTEND;TZID=America/Los_Angeles:20230706T100000
DTSTAMP:20231011T032435Z
CREATED:20231011T032435Z
LAST-MODIFIED:20231011T032435Z
UID:14161-1688634000-1688637600@www.cecs.uci.edu
SUMMARY:Self-aware Memory Management for Emerging Energy-Efficient Architectures
DESCRIPTION:Name: Biswadip (Deep) Maity \nChair: Nikil Dutt \nDate: July 6\, 2023 \nTime: 9:00 AM \nLocation: DBH 3011 \nCommittee: Fadi Kurdahi\, Nalini Venkatasubramanian \nTitle: Self-aware Memory Management for Emerging Energy-Efficient Architectures \nAbstract: \nThe ever-increasing demands of data-intensive applications have presented significant challenges in memory performance and energy efficiency. Traditional approaches to memory management\, relying on workload-specific optimizations and static memory configurations\, have become inadequate in addressing the dynamic nature of modern computing systems. Inspired by principles from psychology and neuroscience\, computational self-awareness aims to develop intelligent systems capable of learning from past experiences\, reasoning about their current state\, and making informed decisions at runtime. My research investigates the application of computational self-awareness in memory management\, with a specific focus on understanding the impact of different degrees of self-awareness within the memory subsystem. By applying self-awareness principles to memory management\, we show that it is possible to create an energy-efficient memory subsystem that adapts to changing workloads and optimizes memory usage across various architectures and layers of the system abstraction. \nShort Bio: \nBiswadip Maity is a Ph.D. candidate at the Computer Science department of University of California\, Irvine. Previously\, he was a Software Engineer at Microsoft. His research is in the area of runtime resource allocation and power management for data-centric architectures with a special focus on self-aware embedded systems\, memory systems\, system software and architecture for heterogeneous systems. He is a member of the Dutt Research Group working with Prof. Nikil Dutt. His work spans across resource-constrained microcontrollers\, IoT systems and Cloud computing performance (in collaboration with Facebook). Biswadip Maity is a Charpak Scholar\, a DAAD Scholar\, and a recipient of a Facebook Research Grant.
URL:https://www.cecs.uci.edu/event/self-aware-memory-management-for-emerging-energy-efficient-architectures/
LOCATION:DBH 3011\, Donald Bren Hall University of California\, Irve\, Irvine\, CA\, 92617\, United States
CATEGORIES:PhD Defenses
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230524T150000
DTEND;TZID=America/Los_Angeles:20230524T160000
DTSTAMP:20231011T032435Z
CREATED:20231011T032435Z
LAST-MODIFIED:20231011T032435Z
UID:14158-1684940400-1684944000@www.cecs.uci.edu
SUMMARY:Integrating Machine Learning for Enhanced Cross-Layer Security and Reliability in Hardware and Cyber-Physical Systems
DESCRIPTION:Name: Rozhin Yasaei\nChair: Mohammad Al Faruque \nDate: May 24\, 2023 \nTime: 3:00 PM \nLocation: EH 2430-Harut Barsamian \nCommittee: Fadi Kurdahi\, Zhou Li \nTitle: Integrating Machine Learning for Enhanced Cross-Layer Security and Reliability in Hardware and Cyber-Physical Systems \nAbstract: \nThis Ph.D. thesis presents a comprehensive investigation into addressing security and reliability challenges in hardware design and cyber-physical systems. The research leverages advanced techniques such as Graph Neural Networks (GNN) and machine learning to develop novel methodologies for anomaly detection\, hardware Trojan detection\, IP piracy detection\, and cross-layer security analysis.\nThe thesis focuses on hardware security\, specifically Hardware Trojan (HT) detection. A golden reference-free approach\, GNN4TJ\, is proposed for detecting HTs at the Register Transfer Level (RTL). By modeling hardware designs as graphs and employing GNN\, GNN4TJ achieves high recall rates for identifying both known and unknown HTs. \nMoving to the IoT domain\, the thesis addresses security challenges in IoT systems through the development of adaptive anomaly detection methods. IoT-CAD utilizes IoT sensor data and fog computing to ensure data integrity and detect anomalous incidents. The proposed methodology incorporates sensor association algorithms\, LSTM neural networks\, and Gaussian estimation for real-time anomaly detection. The thesis further extends the research to multi-modal data fusion\, where the integration of sensor and communication data using GNN enables improved anomaly detection\, source identification\, and recovery in IoT systems. \nIn addition\, the thesis explores the security aspect of wearable devices. A machine learning-based approach is proposed to detect anomalies in wearable devices that rely on NFC protocols. By leveraging system context and sensor correlations\, the methodology achieves high accuracy in detecting anomalies from multiple wearable sensors.\nFurthermore\, the thesis delves into IP piracy detection\, introducing a novel methodology that utilizes GNN to assess circuit similarities and detect IP piracy. The proposed approach proves effective in detecting IP piracy even in the presence of obfuscation techniques\, providing a robust solution for protecting intellectual properties. \nOverall\, this thesis showcases the application of advanced techniques such as GNN and machine learning in enhancing security and reliability in hardware design\, IoT systems\, and wearable devices. The proposed methodologies for anomaly detection\, hardware Trojan detection\, IP piracy detection\, and cross-layer security analysis contribute to advancing the state-of-the-art in ensuring the integrity and security of critical systems in the digital era. \nShort Bio: \nRozhin Yasaei is currently a Ph.D. candidate at the University of California Irvine\, studying computer engineering under Professor Mohammad Al Faruque. She received her M.Sc. degree in computer engineering from the University of California Irvine in 2021 and her B.Sc. in electrical engineering from Sharif University of technology in 2018. Her research interests lie in applied machine learning for embedded and cyber-physical systems security. She develops new methodologies that bring principles and techniques from machine learning\, such as graph neural networks\, to cross-layer system security. Rozhin conducts research that spans the areas of hardware\, embedded systems\, and cyber-physical systems while maintaining relevance to security and reliability. \nRozhin was selected for UC Irvine’s pedagogical fellowship in 2021 and\, through one year of training\, received a teaching excellence certificate and the qualification to design and implement a 3-day training program for incoming graduate students to prepare them for teaching assistance. Furthermore\, she received the Rising Stars in EECS Award in 2022\, the iREDEFINE Professional Development Award in 2022\, UCI UROP Research Discovery Fellowship in 2022\, UCI Henry Samueli Endowed Scholarship in 2018\, and several scholarships from different conferences. \nRozhin is devoted to promoting diversity and inclusion and supporting her fellow collages\, especially the underrepresented population in STEM\, by actively volunteering for panels\, workshops\, and conferences with similar visions and serving the community. She has been the president of the UCI chapter of the Women in CyberSecurity (WiCyS) organization since 2021 to foster women’s participation in this field. Qualified with mentoring excellence certificate\, she has been selected by several programs at UCI to mentor undergraduate and graduate students.
URL:https://www.cecs.uci.edu/event/integrating-machine-learning-for-enhanced-cross-layer-security-and-reliability-in-hardware-and-cyber-physical-systems/
LOCATION:EH 2430\, Engineering Hall\, University of California\, Irvine\, Irvine\, CA\, 92697\, United States
CATEGORIES:PhD Defenses
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230523T140000
DTEND;TZID=America/Los_Angeles:20230523T150000
DTSTAMP:20231011T032546Z
CREATED:20231010T160244Z
LAST-MODIFIED:20231011T032546Z
UID:13940-1684850400-1684854000@www.cecs.uci.edu
SUMMARY:Reasoning About Emergent Behaviors at Runtime
DESCRIPTION:Name: Caio Batista de Melo\nChair: Nikil Dutt \nDate: May 23\, 2023 \nTime: 2:00 PM \nLocation: DBH 4011 \nCommittee: Fadi Kurdahi\, Tony Givargis \nTitle: Reasoning About Emergent Behaviors at Runtime \nAbstract: \nAs Autonomous Vehicles (AVs) become more common in the market\, the development of new technologies and solutions for them advances quickly. However\, there still are safety concerns regarding the increasing presence of AVs on public roads. For example\, the Cali- fornia Department of Motor Vehicles reported an approximate average of 3 disengagement events for each AV under test in 2021 and 1 event for every 1\,500 driven miles. Considering that a vehicle drives\, on average\, 15\,000 miles per year\, an AV would have ten such incidents annually. With this in mind\, work needs to be done to enable AVs to handle unexpected sit- uations (i.e.\, emergent behaviors) that can arise while driving\, increasing the safety for both humans and AVs on the road. This dissertation discusses possible approaches to improve the detection and reasoning of emergent behaviors at runtime. \nFirst\, it describes SAFER\, a framework to monitor individual systems using runtime verifica- tion alongside machine learning anomaly detection to detect emergent behaviors at runtime. SAFER achieves an average F1-score of 83% over four different example systems\, showcas- ing its efficacy to be on par with related work while being applicable to different classes of applications. Next\, it discusses DRIVE\, a framework for monitoring collective systems that can work together to find global anomalies that affect collective safety by identifying violations in local properties. Throughout a state-of-the-art inspired truck platooning case study\, DRIVE can detect all safety property violations\, with most queries resolved under 1 ms and a maximum latency of 11 ms per query\, demonstrating its promise. Lastly\, it presents LOCoCAT\, a low-overhead framework for emergent behavior reasoning to classify anomaly types based on vehicular data. LOCoCAT achieves an F1-score of up to 99.16% within the first 50 ms of the anomaly\, allowing the system to react quickly. These three techniques can be combined to allow future AVs to operate more safely on the road. \nShort Bio: \nCaio Batista del Melo is a Ph.D. candidate at the Computer Science Department of the University of California\, Irvine\, where he works with Prof. Nikil Dutt on the Information Processing Factory project. His research focuses on emergent behaviors and fault detection of systems. From a big-picture view\, he explored how we can enable self-driving vehicles to detect unexpected situations and decide how to best react to them. Prior to joining UCI\, he attended the University of Brasília\, in Brazil\, where he got a B.Sc. and an M.Sc. in Computer Science. While at UnB\, he worked with Prof. Genaína Rodrigues researching dependable distributed systems\, more specifically\, implied scenarios.
URL:https://www.cecs.uci.edu/event/reasoning-about-emergent-behaviors-at-runtime/
LOCATION:DBH 4011\, Donald Bren Hall\, University of California\, Irvine\, Irvine\, CA\, 92617\, United States
CATEGORIES:PhD Defenses
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230522T090000
DTEND;TZID=America/Los_Angeles:20230522T110000
DTSTAMP:20231011T032434Z
CREATED:20231011T032434Z
LAST-MODIFIED:20231011T032434Z
UID:14157-1684746000-1684753200@www.cecs.uci.edu
SUMMARY:Graph Learning for Robust Embedded and Cyber-Physical Systems
DESCRIPTION:Name: Shih-Yuan Yu\nChair: Mohammad Al Faruque \nDate: May 22\, 2023 \nTime: 9:00 AM – 11:00 AM \nLocation: EH 2430 \nCommittee: Pramod Khargonekar\, Yanning Shen \nTitle: Graph Learning for Robust Embedded and Cyber-Physical Systems \nAbstract: \nSince the appearance of microprocessors\, miscellaneous categories of computer systems\, including Embedded and Cyber-Physical Systems (ECPS)\, have become an integral part of our modern society. In contrast to general-purpose computers\, an ECPS is a computing system designed to perform a dedicated or narrow range of functions with minimal human intervention. Today\, the advanced standards in device networking\, ubiquitous access to the Internet\, miniaturization of processors\, and reduced power consumption have taken this field to the next level. However\, despite these technological growths\, designing a robust ECPS remains an open research challenge where the goal is to achieve better functionalities\, encompass complex\, uncertain\, and changing environments\, and ensure system security. Minor failures on ECPS may cause severe collapses or cyberattacks\, impeding progress toward increasing automation and modernizing our computing environments. \nTo achieve robustness\, this dissertation studies the embedding of Computational Intelligence (CI) into emerging Graph Learning (GL) technologies. The CI paradigms mimic the nature of humans\, aiming at solving complex problems and exhibiting a cognitive ability to learn or adapt to new situations to generalize\, abstract\, discover\, and associate. As a prerequisite of this research direction\, Machine Learning (ML) has become increasingly ubiquitous\, with existing works exploring various fields\, from self-driving vehicles\, facial recognition systems\, and real-time language translation to security surveillance\, innovative home applications\, and health monitoring. However\, conventional ML algorithms typically require appropriate vectorized representations crafted by domain experts to accomplish the desired goals. Graph-structured data have imposed unprecedented challenges on ML due to their inherent complexity. Unlike text\, audio\, and images\, graphs are embedded in an irregular dimension\, making some essential operations of ML inapplicable. GL has attracted much attention to new research ideas in several fields. To date\, many researchers have proven the usefulness of GL in social computing\, information retrieval\, computer vision\, bioinformatics\, economics\, and e-commerce. However\, its applications in subfields of ECPS still need to be explored. \nIn this dissertation\, we will cover GL applications for ECPS in robust Integrated Circuit (IC) design analysis for hardware security\, robust binary analysis for enabling software security\, and ultimately how GL brings the scene-understanding capabilities of autonomous driving systems to the next level. In IC design analysis (Chapter 2)\, we explore how GL can be leveraged to resolve challenging problems in hardware security. We propose HW2VEC and demonstrate how it can be utilized for Hardware Trojan (HT) detection and Intellectual Property (IP) piracy detection. Next\, in the binary analysis (Chapter 3)\, we demonstrate how a more advanced GL methodology\, called CFG2VEC\, can reverse-engineer the semantic source-level information lost during the compilation process\, thus making the binary software patching tasks more efficient. Then\, we describe our novel methodology\, SG2VEC\, to enhance autonomous driving systems’ scene understanding capabilities (Chapter 4). To enable this cognitive capability\, we propose to use scene-graph to encode the surrounding traffic participants and a pipeline of spatiotemporal scene-graph embedding networks to process scene-graphs and learn toward goals. During the courses of these projects\, we have developed many research tools to confront the technical challenges (mentioned in Appendices). \nShort Bio: \nShih-Yuan Yu is a 5-th year Ph.D. candidate in Computer Engineering and works in the Autonomous and Intelligent Cyber-Physical Systems lab under the supervision of Professor Mohammad Al Faruque. He received his B.S. and M.S. degrees in Computer Science and Information Engineering from the National Taiwan University in 2014. Before his Ph.D.\, Shih-Yuan worked as a software engineer at MediaTek for 4+ years. During his Ph.D.\, he interned at TuSimple (an autonomous driving company) and Siemens (industrial-academia collaborated projects). During his Ph.D.\, he has published 20+ papers in conferences and journals. His interests are making embedded cyber-physical systems more robust with data-driven methodologies enabled by graph learning.
URL:https://www.cecs.uci.edu/event/graph-learning-for-robust-embedded-and-cyber-physical-systems/
LOCATION:EH 2430\, Engineering Hall\, University of California\, Irvine\, Irvine\, CA\, 92697\, United States
CATEGORIES:PhD Defenses
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230505T153000
DTEND;TZID=America/Los_Angeles:20230505T163000
DTSTAMP:20231011T032408Z
CREATED:20231011T032408Z
LAST-MODIFIED:20231011T032408Z
UID:14156-1683300600-1683304200@www.cecs.uci.edu
SUMMARY:Context-Aware and Energy-Efficient Autonomous Systems
DESCRIPTION:Name: Arnav Malawade\nChair: Mohammad Al Faruque \nDate: May 5\, 2023 \nTime: 3:30 PM \nLocation: ISEB 1200 \nCommittee: Marco Levorato\, Yasser Shoukry \nTitle:  Context-Aware and Energy-Efficient Autonomous Systems \nAbstract: \nAutonomous systems (AS) are beginning to play a more significant role in modern society. AS include robotic systems that perceive and react to changes in their environment\, such as aerial drones\, ground/aquatic robots\, and consumer autonomous vehicles (AVs)\, among others. Breakthroughs in deep learning and increased consumer interest in ubiquitous autonomy have fueled recent advances in perception\, modeling\, and control algorithms. However\, these advances come with rising energy costs. Modern AS require large deep-learning (DL) models to perceive the environment and safely detect and avoid objects. The computational demands of these models significantly increase the hardware requirements of the complete system\, such that modern AV systems can require several kilowatts of power to enable autonomy\, reducing range and utility. These impacts affect most AS since AVs\, ground robots\, and drones are typically edge devices that operate in energy-constrained environments. \nThe need for large\, complex DL models is primarily driven by the fact that\, in the real world\, challenging and unpredictable scenarios can occur and must be modeled appropriately by the AS. However\, existing approaches are exceedingly cautious\, preferring to execute a large\, inefficient model even in typical scenarios just in case a difficult edge case arises. This approach to autonomy hinders both the utility of existing AS and the broad-scale adoption of AS. Instead\, an AS should be able to understand the context of its surrounding environment and adapt its model to fit each situation. \nThis dissertation explores methods for developing context-aware AS to improve perception and prediction performance\, reduce energy consumption\, and enable adaptation to dynamic environments. Three different perspectives are studied: (i) scene-graph embedding approaches are proposed and evaluated for improved semantic understanding and state estimation; (ii) methods for implementing split ML models for dynamic\, resource-efficient computation offloading from edge to cloud within real-time latency constraints are studied; and (iii) context-aware\, dynamic ML perception models that jointly optimize performance and energy efficiency are studied from an algorithmic and system-wide optimization perspective. Overall\, the results show that context-aware models achieve state-of-the-art performance across applications\, and dynamic architectures conditioned on context can help resolve the energy-performance trade-off by enabling the joint optimization of these objectives. \nShort Bio: \nArnav Malawade is a PhD Candidate in Computer Engineering and works in the Autonomous and Intelligent Cyber-Physical Systems (AICPS) Lab under the supervision of Professor Mohammad Al Faruque. He received his B.S. in Computer Science and Engineering and his M.S. in Computer Engineering at UC Irvine in 2018 and 2021\, respectively. He was a Doctorate Intern at HRL Laboratories in 2022. His research interests include autonomous driving\, graph-based perception\, dynamic neural networks\, and smart manufacturing.
URL:https://www.cecs.uci.edu/event/context-aware-and-energy-efficient-autonomous-systems/
LOCATION:ISEB 1200\, Interdisciplinary Science and Engineering Building University of California\, Irve\, Irvine\, CA\, 92697\, United States
CATEGORIES:PhD Defenses
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230501T120000
DTEND;TZID=America/Los_Angeles:20230501T140000
DTSTAMP:20231011T032408Z
CREATED:20231011T032408Z
LAST-MODIFIED:20231011T032408Z
UID:14155-1682942400-1682949600@www.cecs.uci.edu
SUMMARY:Cross-Layer Attacks and Defenses in the Cyber-Physical System (CPS)
DESCRIPTION:Name: Anomadarshi Barua\nChair: Mohammad Abdullah Al Faruque \nDate: May 1\, 2023 \nTime: 12 PM – 2 PM \nLocation: EH 2430-Harut Barsamian \nCommittee: Pramod P Khargonekar\, Zhou Li \nTitle: Cross-Layer Attacks and Defenses in the Cyber-Physical System (CPS) \nAbstract: \nHave you ever wondered how simple music can leak deadly pathogens from a bio-safety lab? Have you ever considered how an industrial robotic arm can be compromised from a cloud infrastructure? Have you ever imagined an attacker could use a magnetic field to shut down a weak microgrid?  If the above questions excite you\, please attend my PhD oral defense\, where my research answers the above questions with a practical demonstration of CPS attacks on industry-used infrastructure. With this seminar\, the audience will gain a deep insight into how hardware/software interaction in CPS’s cross-layer can open a “Pandora’s Box” of unknown threats that can come from very unconventional ways. \nMy research sits at the intersection of hardware and software cross-layers in CPSs\, exploring how interactions of cyber and physical components can affect the safety and controllability of closed-loop control from sensing to actuation. Most attacks on CPSs can be propagated from the physical domain to the cyber domain or vice-versa and hence\, can be termed cross-layer attacks. To understand these cross-layer attacks\, a very different set of methodologies and tools is needed. My research talks about how these tools can be made. Moreover\, the seminar will show how cross-layer vulnerabilities can be handled using new hardware/software co-design approaches to detect\, contain and isolate threats in CPSs. \nShort Bio: \nAnomadarshi Barua is a Ph.D. candidate studying Computer Engineering at the University of California Irvine. He received his M.S. degree in Embedded Computing Systems jointly at the University of Southampton (UoS) and the Norwegian University of Science and Technology (NTNU) in 2016. Now\, He is working under the supervision of Professor Al Faruque in the Autonomous and Intelligent Cyber-Physical Systems (AICPS) Lab. His current research focuses on the system-level design of embedded and Cyber-Physical-Systems (CPSs) with a special interest in CPS security\, low-power design\, and computer microarchitecture. He publishes in the security conferences like ACM CCS\, USENIX\, ACSAC\, RAID\, HOST\, and CHES and system conferences like ICCPS and ICCAD. He also has worked in several industries in North America and Europe\, such as Intel Corporation (USA)\, Solidigm (USA)\, Nordic Semiconductor (Norway)\, and IDEAS (Norway). In his leisure time\, he likes to travel to new places and go for hiking.
URL:https://www.cecs.uci.edu/event/cross-layer-attacks-and-defenses-in-the-cyber-physical-system-cps/
LOCATION:EH 2430\, Engineering Hall\, University of California\, Irvine\, Irvine\, CA\, 92697\, United States
CATEGORIES:PhD Defenses
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230222T103000
DTEND;TZID=America/Los_Angeles:20230222T113000
DTSTAMP:20231011T032408Z
CREATED:20231011T032408Z
LAST-MODIFIED:20231011T032408Z
UID:14152-1677061800-1677065400@www.cecs.uci.edu
SUMMARY:Online Learning for Orchestrating Deep Learning Inference at Edge
DESCRIPTION:Name: Sina Shahhosseini \nChair: Nikil Dutt \nDate: February 22\, 2023 \nTime: 10:30 AM \nLocation: 2011 DBH \nCommittee: Amir Rahmani\, Fadi Kurdahi \nTitle:  Online Learning for Orchestrating Deep Learning Inference at Edge \nAbstract:  \nDeep-learning-based intelligent services have become prevalent in cyber-physical applications including smart cities and health-care. Resource-constrained end-devices must be carefully managed in order to meet the latency and energy requirements of computationally-intensive deep learning services. Collaborative end-edge-cloud computing for deep learning provides a range of performance and efficiency that can address application requirements through computation offloading. The decision to offload computation is a communication-computation co-optimization problem that varies with both system parameters (e.g.\, network condition) and workload characteristics. On the other hand\, deep learning model optimization provides another source of tradeoff between latency and model accuracy. An end-to-end decision-making solution that considers such computation-communication problem is required to synergistically find the optimal offloading policy and model for deep learning services. To this end\, we propose a reinforcement-learning-based computation offloading solution that learns optimal offloading policy considering deep learning model selection techniques to minimize response time while providing sufficient accuracy. In addition\, we present a Hybrid Learning approach that reduces the number of interactions with the system environment by combining model-based and model-free reinforcement learning. We demonstrate the efficacy of our strategies through experimental comparison with state-of-the-art RL-based inference orchestration. \nShort Bio: \nSina Shahhosseini received his B.S. and M.S degrees in Electrical Engineering from the University of Tehran and Sharif University of Technology\, in 2013 and 2015\, respectively. Since Fall 2016\, he has been working towards his Ph.D. degree in Computer Engineering at University of California\, Irvine\, with a focus on the application of reinforcement learning for task orchestration in computer systems. He interned with Cruise in the summer of 2022 as a Machine Learning Engineer Intern\, where he worked on optimizing Machine Learning frameworks for autonomous vehicles.
URL:https://www.cecs.uci.edu/event/online-learning-for-orchestrating-deep-learning-inference-at-edge/
LOCATION:DBH 2011\, Donald Bren Hall University of California\, Irve\, Irvine\, CA\, 92617\, United States
CATEGORIES:PhD Defenses
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20220817T103000
DTEND;TZID=America/Los_Angeles:20220817T113000
DTSTAMP:20231011T032408Z
CREATED:20231011T032408Z
LAST-MODIFIED:20231011T032408Z
UID:14153-1660732200-1660735800@www.cecs.uci.edu
SUMMARY:An End-to-End Platform for Multi-Modal Machine Learning Affective Computing Services
DESCRIPTION:Name: Emad Kasaeyan Naeini \nChair: Nikil Dutt \nDate: August 17\, 2022 \nTime: 10:30 AM \nLocation: DBH 3011 \nCommittee: Amir Rahmani\, Fadi Kurdahi \nTitle:  An End-to-End Platform for Multi-Modal Machine Learning Affective Computing Services \nAbstract: \nSmart affective computing applications deliberately influence pain\, emotion and other affective phenomena\, and are fundamental to human experience\, health and well-being. Affective states such as pain\, stress\, and emotion are intrinsically subjective in nature\, posing challenges for objective assessment and quantification of such affective phenomena. Prior efforts in affective computing provide a foundation for the automated analysis of affective states\, but still face challenges in real-life\, everyday settings. We use pain as an exemplar for affective computing services. Pain assessment is critical for optimal treatment\, and is particularly important during periods of acute pain since inadequately treated acute pain increases the risk of chronic pain. Historically\, patients have served as the main assessment tool as they are able to self-report their pain presence and severity on standard\, but somewhat subjective pain scales. However\, it remains a challenge to assess pain from patients who cannot self-report. Automatic pain recognition systems could be crucial to facilitating accurate\, objective\, and real-time pain measurement\, that can in turn improve pain management and ultimately lead to improved patient outcomes. Using pain as an exemplar of smart affective computing services\, this thesis proposes the use of multimodal sensing of physiological and behavioral input data\, transmits the data to edge and/or cloud nodes\, and processes data with compute-intensive machine learning (ML) algorithms. The efficiency of ML-driven applications for affective computing (e.g.\, pain assessment) is greatly affected by run-time variations resulting from continuous stream of noisy input data\, unreliable network connections\, and the variations in computational requirements of ML algorithms. Towards that end\, this thesis evaluates and automates objective and real-time multimodal pain assessment algorithms\, and performs design space exploration of accuracy-performance-energy trade-offs and sense-compute co-optimization for multimodal machine learning (MMML) methods. The approach developed in this thesis could be used for the design and development of an end-to-end platform for multimodal machine learning affective computing services\, together with a thorough analysis of their roles in the prediction\, performance and energy in future studies. \nShort bio: \nEmad Kasaeyan Naeini is a Ph.D. candidate in Computer Engineering at the University of California\, Irvine. He received his M.Sc. degree in Electrical and Computer Engineering from the Department of Electrical Engineering and Computer Science at the University of California Irvine in 2020 and his B.Sc. in Electrical Engineering from Sharif University of Technology\, Tehran\, Iran\, in 2017. His research interests primarily include deep learning and machine learning\, signal processing\, healthcare Internet of Things (IoT)\, and healthcare data analysis.
URL:https://www.cecs.uci.edu/event/an-end-to-end-platform-for-multi-modal-machine-learning-affective-computing-services/
LOCATION:DBH 3011\, Donald Bren Hall University of California\, Irve\, Irvine\, CA\, 92617\, United States
CATEGORIES:PhD Defenses
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20220707T100000
DTEND;TZID=America/Los_Angeles:20220707T230000
DTSTAMP:20231011T032340Z
CREATED:20231011T032340Z
LAST-MODIFIED:20231011T032340Z
UID:14136-1657188000-1657234800@www.cecs.uci.edu
SUMMARY:Transaction-Level Modeling of Deep Neural Networks for Efficient Parallelism and Memory Accuracy
DESCRIPTION:Name: Emad Arasteh\nChair: Prof. Rainer Doemer \nDate: July 7\, 2022 \nTime: 10:00 am \nLocation: EH 3206 \nCommittee: Prof. Fadi Kurdahi\, Prof. Ian Harris \nTitle: Transaction-Level Modeling of Deep Neural Networks for Efficient Parallelism and Memory Accuracy \nAbstract:  \nThe emergence of data-intensive applications\, such as Deep Neural Networks (DNNs)\, exacerbates the well-known memory bottleneck in computer systems and demands early attention in the design flow. Electronic System-Level (ESL) design using SystemC Transaction Level Modeling (TLM) enables effective performance estimation\, design space exploration\, and gradual refinement. In this dissertation\, we present our exploratory modeling framework for hardware-software codesign based on SystemC TLM with particular focus on exposing parallelism and memory contention. We demonstrate the effectiveness of our approach for representative complex DNNs such as GoogLeNet and Single Shot MultiBox Detector. \nFirst\, we study the impact of communication mechanisms on the available parallelism in TLM models. Specifically\, we demonstrate the impact of varying synchronization mechanisms and buffering schemes on the exposed parallelism using different modeling styles of a DNN. We measure the performance of aggressive out-of-order parallel discrete event simulation and analyze the available parallelism in the models. Our study suggests that increased parallel simulation performance indicates better models with higher amounts of parallelism exposed. \nSecond\, we explore the critical aspects of modeling and analysis of timing accuracy and memory contention. A major hurdle in tackling the memory bottleneck is the detection of memory contention late in the design cycle once detailed timed or cycle-accurate models are developed. A memory bottleneck detected at such a late stage can severely limit the available design choices or even require costly redesign. To explore new architectures prior to RTL implementation\, we propose a novel TLM-2.0 loosely-timed contention-aware (LT-CA) modeling style that offers high-speed simulation close to traditional loosely-timed (LT) models\, yet shows the same accuracy for memory contention as low level approximately-timed (AT) models. \nFinally\, we refine further the TLM-2.0 AT model by adding a cycle-accurate model of a memory subsystem. This model provides a higher timing accuracy for contention analysis\, hence it gives a more accurate estimation of the performance. We revise our LT-CA memory delay modeling to provide further accuracy comparable to the cycle-accurate TLM model of the shared memory subsystem. The high amount of contention on the shared memory suggests new processor architectures with local memories. \n\nShort Bio: \nEmad Arasteh is a Ph.D. candidate in Computer Engineering at University of California\, Irvine and a graduate researcher at the Center for Embedded and Cyber-physical Systems (CECS). He received the M.Sc. degree in Electronic Design from Lund University in Sweden. His current research interests include system-level modeling and design of embedded systems\, scalable memory system design\, and massively-parallel processor architecture. Previously\, he worked as a hardware and software engineer at semiconductor companies in Sweden and USA including Ericsson\, Canon (Axis Communications) and Samsung.
URL:https://www.cecs.uci.edu/event/transaction-level-modeling-of-deep-neural-networks-for-efficient-parallelism-and-memory-accuracy/
LOCATION:EH 3206\, Engineering Hall University of California\, Irve\, Irvine\, CA\, 92697\, United States
CATEGORIES:PhD Defenses
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20220516T090000
DTEND;TZID=America/Los_Angeles:20220516T100000
DTSTAMP:20231011T032339Z
CREATED:20231011T032339Z
LAST-MODIFIED:20231011T032339Z
UID:14132-1652691600-1652695200@www.cecs.uci.edu
SUMMARY:Internet of Things-Based Collaborative Position-Sensing Systems for Cardiopulmonary Resuscitation and Indoor Localization
DESCRIPTION:Name: Chen\, Hsin-Chung “Andrew” \nChair: Pai Chou \nDate:  Wednesday\, March 16\, 2022 \nTime: 9:00 am \nLocation:  https://uci.zoom.us/j/93264053012 \nCommittee: Nader Bagherzadeh\, Rainer Doemer \nTitle: Internet of Things-Based Collaborative Position-Sensing Systems for Cardiopulmonary Resuscitation and Indoor Localization \nAbstract: \nThe ubiquitous IoT devices have promoted the growth of applications that require target positioning. While precise positioning remains a challenge for IoT devices\, existing methods mainly focus on optimizing the positioning algorithms or expanding the sensing modalities. One opportunity area of improvement is to leverage their wireless communication capability to exchange sensing data of different modalities with nearby devices. This work proposes collaborative sensing frameworks to enhance the accuracy of position inference. \nOur approach to collaborative sensing is divided into selection of reliable devices\, the data-exchange mechanism\, and the data-fusion algorithm. We demonstrate our approach on two different position-sensing systems: one for chest compression-depth estimation and one for indoor pedestrian localization. The key components used in the frameworks include quality assessment of sensor data using mutual information\, data-fusion algorithms based on the chest-compression model and the pedestrian-encounter model\, and the data-exchange mechanism using Bluetooth Low Energy (BLE). A real-time position-estimation method based on our chest-compression model is proposed to remove the noise and handle the cumulative error. A collaborative conditional random field algorithm is developed to reduce the convergence distance in the localization estimation. Experimental results show our collaborative-sensing approach outperforms the existing solutions in terms of real-time estimated position. \nThis dissertation proposes an alternative approach to optimizing the position-sensing systems through collaborative sensing. Our work represents the first step towards the universal positioning on IoT devices and provides research opportunities for further improvement and exploration. \n\nShort Bio: Hsinchung Chen is a Ph.D. candidate in computer engineering. His research interests include signal processing\, machine learning\, and embedded system design.
URL:https://www.cecs.uci.edu/event/internet-of-things-based-collaborative-position-sensing-systems-for-cardiopulmonary-resuscitation-and-indoor-localization/
LOCATION:CA
CATEGORIES:PhD Defenses
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20200529T140000
DTEND;TZID=America/Los_Angeles:20200529T150000
DTSTAMP:20231011T190957Z
CREATED:20231011T190957Z
LAST-MODIFIED:20231011T190957Z
UID:14579-1590760800-1590764400@www.cecs.uci.edu
SUMMARY:Advancing Compiler and Simulator Techniques for Highly Parallel Simulation of Embedded Systems
DESCRIPTION:Name: Zhongqi Cheng\nChair: Prof. Rainer Doemer \nDate: May 29th\, 2020 \nTime: 02:00 PM \nLocation:  Zoom \nCommittee: Rainer Doemer (Chair)\, Prof. Mohammad Al Faruque and Prof. Aparna Chandramowlishwaran \nTitle: “Advancing Compiler and Simulator Techniques for Highly Parallel Simulation of Embedded Systems” \nAbstract: \nAs an Electronic System Level (ESL) design language\, the IEEE SystemC standard is widely used for testing\, validation and verification of embedded system models. Discrete Event Simulation (DES) has been used for decades as the default SystemC simulation semantic. However\, due to the sequential nature of DES\, Parallel DES has recently gained an increasing amount of attention for performing high speed simulations on parallel computing platforms. To further exploit the parallel computation power of modern multi- and many-core platforms\, Out-of-order Parallel Discrete Event Simulation (OoO PDES) has been proposed. In OoO PDES\, threads comply with a partial order such that different simulation threads may run in different time cycles to increase the parallelism of execution. The Recoding Infrastructure for SystemC (RISC) has been introduced as a tool flow to fully support OoO PDES. \nTo preserve the SystemC semantics under OoO PDES\, a compiler based approach statically analyzes the race conditions in the input model. However\, there are severe restrictions: the source code for the input design must be available in one file\, which does not scale. This disables the use of Intellectual Property (IP) and hierarchical file structures. In this dissertation\, we propose a partial-graph based approach to scale the static analysis to support separate files and IP reuse. Specifically\, the Partial Segment Graph (PSG) data structure is proposed and is used to abstract the behaviours and communication of modules within a single translation unit. These partial graphs are combined at top level to reconstruct the complete behaviors and communication of the entire model. \nWe also propose new algorithms to support the static analysis for modern SystemC TLM-2.0 standard. SystemC TLM-2.0 is widely used in industrial ESL designs for better interoperability and higher simulation speed. However\, it is identified as an obstacle for parallel SystemC simulation due to the disappearance of channels. To solve the problem\, we propose a compile time approach to statically analyze potential conflicts among threads in SystemC TLM-2.0 loosely- and approximately-timed models. A new Socket Call Path (SCP) technique is introduced which provides the compiler with socket binding information for precise static analysis. Based on SCP\, an algorithm is proposed to analyze entangled variable pairs for automatic and accurate conflict analysis. \nBesides the works on the compiler side\, we focus as well on increasing the simulation speed of OoO PDES. We observe that the granularity of the Segment Graph (SG) data structure used in static analysis has a high impact on OoO PDES. This motivates us to propose a set of coding guidelines for the RISC users to properly refine their SystemC model for a higher simulation speed. \nFurthermore\, in this dissertation\, an algorithm is proposed to optimize directly the event delivery strategy in OoO PDES. Event delivery in OoO PDES was very conservative\, which often postponed the execution of waiting threads due to unknown future behaviors of the SystemC model\, and in turn became a bottleneck of simulation speed. The algorithm we propose takes advantage of the prediction of future thread behaviors\, and therefore allows waiting threads to resume execution earlier\, resulting in significantly increased simulation speed. \nTo summarize\, the contributions of this dissertation include: 1) a scalable RISC tool flow for statically analyzing and protecting 3rd party IPs in models with multiple files\, 2) an advanced static analysis approach for modern SystemC TLM-2.0 models\, 3) a set of coding guidelines for RISC users to achieve higher simulation speed\, and 4) a more efficient event delivery algorithm in OoO PDES scheduler using prediction information. \nTogether\, these compiler and simulator advances enable OoO PDES for larger and modern model simulation and thus improve the design of embedded systems significantly\, leading to better devices at lower cost in the end. \n 
URL:https://www.cecs.uci.edu/event/advancing-compiler-and-simulator-techniques-for-highly-parallel-simulation-of-embedded-systems/
LOCATION:CA
CATEGORIES:PhD Defenses
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20200310T093000
DTEND;TZID=America/Los_Angeles:20200310T103000
DTSTAMP:20231011T190957Z
CREATED:20231011T190957Z
LAST-MODIFIED:20231011T190957Z
UID:14578-1583832600-1583836200@www.cecs.uci.edu
SUMMARY:Design and Implementation of Robust Full-Duplex Wireless Network
DESCRIPTION:Name: Sergey Shaboyan\nChair: Prof. Ahmed Eltawil \nDate: March 10th\, 2020 \nTime: 09:30 AM \nLocation:  EH 3206 \nCommittee: Prof. Ahmed Eltawil (Chair)\, Prof. Ender Ayanoglu\, Prof. Zhiying Wang \nTitle: “Design and Implementation of Robust Full-Duplex Wireless Network” \nAbstract: \nRecently Full-Duplex (FD) communication has gained significant interest due to demonstrable increase in throughput and spectral efficiency. Conventional Half-duplex (HD) communication systems use either time-duplexing or frequency-duplexing to avoid self-interference. In contrast\, full-duplex systems transmit and receive simultaneously on the same frequency band\, thus optimally utilizing available resources. The main challenge in FD systems is managing the self-interference (SI) signal at each node\, which is typically orders of magnitude larger than the intended signal of interest (SOI). To achieve sufficient SI suppression\, FD systems rely on cancellation across multiple domains such as spatial\, analog\, and digital. However\, number of practical\, FD specific challenges arise impacting quality of service\, when at least one node in a network operates in full-duplex mode. \nIn this thesis\, we consider practical issues of wireless networks containing a full-duplex node. The ultimate goal of this work is to design and implement real-time\, end-to-end networks consisting of at least one FD node that is capable of improving network performance under limited available bandwidth constraint. First\, we identify synchronization issues in a network consisting of a full-duplex base station communicating with half-duplex nodes. Novel synchronization techniques specific for full-duplex networks are proposed that allow compensation of synchronization errors in time and frequency. The proposed techniques are implemented and tested experimentally on a real-time full-duplex wireless network. Second\, we characterize the dynamic environment impact on the received self-interference in the FD system\, which is equipped with a reconfigurable antenna as a passive SI suppression mechanism. The self-interference channel delay profile is measured using the FD system operating on 5MHz\, 10MHz\, and 20MHz bandwidths. The measured channel profiles collected under suppressing and non-suppressing antenna patterns are compared\, and channel changes in static\, as well as dynamic environments\, are highlighted. We then statistically model the SI channel by performing probability distribution fitting to SI channel data. Third\, the thesis proposes a Wi-Fi compliant self-interference active cancellation technique for amplify and forward\, as well as decode and forward full-duplex relays. Finally\, we design and implement an end-to-end wireless network extended with the aid of a custom-designed amplify and forward full-duplex relay. We then analyze the relay coverage limitation under the stability and transmit power constraints. The network performance is analyzed as a function of relay location for constant gain and constant transmit power modes\, consequently suggesting optimal relay location that will maximize signal to noise plus interference ratio (SINR) at the destination node. We evaluate the overall network performance by simulation\, as well as experimentally in outdoor/indoor environments.
URL:https://www.cecs.uci.edu/event/design-and-implementation-of-robust-full-duplex-wireless-network/
LOCATION:CA
CATEGORIES:PhD Defenses
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20200220T100000
DTEND;TZID=America/Los_Angeles:20200220T110000
DTSTAMP:20231011T190957Z
CREATED:20231011T190957Z
LAST-MODIFIED:20231011T190957Z
UID:14577-1582192800-1582196400@www.cecs.uci.edu
SUMMARY:Novel Monitoring\, Detection\, and Optimization Techniques for Neurological Disorders
DESCRIPTION:Name: Seyede Mahya Safavi\nChair: Prof. Pai Chou \nDate: February 20th\, 2020 \nTime: 10:00 AM \nLocation:  EH 3404 \nCommittee: Prof. Pai Chou (Chair)\, Prof. Beth Lopour\, Prof. Phillip Sheu \nTitle: “Novel Monitoring\, Detection\, and Optimization Techniques for Neurological Disorders” \nAbstract: \nThe advancement in chronically implanted neural recording devices has led to advent of assisting devices for rehabilitation and restoring lost sensorimotor functions in patients suffering from paralysis. Electrocorticogram (ECoG) signal can record high-Gamma sub-band activity known to be related to hand movements. In the first part of this work\, we Propose a finger movement detection technique based on ECoG source localization. In fact\, the finger flexion and extension are originating in slightly different areas of motor cortex. The origin of the brain activity is used as the distinctive feature for decoding the finger movement. \nThe real-time implementation of brain source localization is challenging due to extensive iterations in the existing solutions. In the second part of this work\, we have proposed two techniques to reduce the computational complexity of the Multiple Signal Classification (MUSIC) algorithm. First the cortex surface is parsed into several regions. Next\, a novel nominating procedure will pick a number of regions to be searched for brain activity. In the second step\, an electrode selection technique based on the Cramer-Rao bound of the errors is proposed to select the best set of an arbitrary number of electrodes. The proposed techniques lead to 90% reduction in computational complexity while maintaining a good concordance in terms of localization error compared to regular MUSIC algorithm. \nEpilepsy is a neurological disorder with multiple comorbid conditions including cardiovascular and respiratory disorders. The cardiovascular imbalance is of great importance since the mechanisms of Sudden Unexpected Death in Epilepsy (SUDEP) is still unknown. The ictal tachycardia is the most well-known cardiac imbalance during the seizure. In the third part of this dissertation\, we used an optical sensor called photoplethysmogram (PPG) to investigate the variations in ictal blood flow in limbs. Six different features related to hemodynamics were derived from PPG pulse morphology. A consistent pattern of ictal change was observed across all the subjects/seizures. These variations suggest an increase in vascular resistance due to an increase in sympathetic tone. The timing analysis of the PPG features revealed some PPG feature variations can precede the ictal tachycardia by 50 seconds. These features were used to train a neural network based on Long Short Term Memory LSTM architecture for automatic seizure detection. We were able to reduce the False Alarm rate by 50% compared to other heart rate variability based detectors.
URL:https://www.cecs.uci.edu/event/novel-monitoring-detection-and-optimization-techniques-for-neurological-disorders/
LOCATION:CA
CATEGORIES:PhD Defenses
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20200206T093000
DTEND;TZID=America/Los_Angeles:20200206T103000
DTSTAMP:20231011T190957Z
CREATED:20231011T190957Z
LAST-MODIFIED:20231011T190957Z
UID:14575-1580981400-1580985000@www.cecs.uci.edu
SUMMARY:Efficient Offline and Online Training of Memristive Neuromorphic Hardware
DESCRIPTION:Name: Mohammed Fouda\nChair: Prof. Ahmed Eltawil \nDate: February 6th\, 2020 \nTime: 9:30 AM \nLocation:  EH 3206 \nCommittee: Ahmed Eltawil (Chair)\, Prof. Fadi Kurdahi\, Prof. Nikil Dutt\, Prof. Emre Neftci \nTitle: “Efficient Offline and Online Training of Memristive Neuromorphic Hardware” \nAbstract: \nBrain-inspired neuromorphic systems have witnessed rapid development over the last decade from both algorithmic and hardware perspectives. Neuromorphic hardware promises to be more energy- and speed- efficient as compared to traditional Von-Neumann architectures. Thanks to the recent progress in solid-state devices\, different nanoscale-nonvolatile memory devices\, such as RRAMs (memristors)\, STT-RAM and PCM\, support computations based on mimicking biological synaptic response. The most important advantage of these devices is their ability to be sandwiched between interconnect wires creating crossbar array structures that are inherently able to perform matrix-vector multiplication (MVM) in one step. Despite the great potential of RRAMs\, they suffer from numerous nonidealities limiting the performance\, including\, high variability\, asymmetric and nonlinear weight update\, endurance\, retention and stuck at fault (SAF) defects in addition to the interconnect wire resistance that creates sneak paths. This thesis will focus on the application of RRAMs for neuromorphic computation while accounting for the impact of device nonidealities on neuromorphic hardware. \n\n\n\n\nIn this thesis\, we propose software-level solutions to mitigate the impact of nonidealities\, that highly affect the offline (ex-situ) training\, without incorporating expensive SPICE or numerical simulations. We propose two techniques to incorporate the effect of sneak path problem during training\, in addition to the device’s variability\, with negligible overhead. The first technique is inspired by the impact of the sneak path problem on the stored weights (devices’ conductances) which we referred to as the mask technique. This mask is element-wise multiplied by the weights during the training. This mask can be obtained from measured weights of fabricated hardware. The other solution is a neural network estimator which is trained by our SPICE-like simulator. The test validation results\, done through our SPICE-like framework\, show significant improvement in performance\, close to the baseline BNNs and QNNs\, which demonstrates the efficiency of the proposed methods. Both techniques show the high ability to capture the problem for multilayer perceptron networks for MNIST dataset with negligible runtime overhead. In addition\, the proposed neural estimator outperforms the mask technique for challenging datasets such as CIFAR10. Furthermore\, other nonidealities such as SAF defects and retention are evaluated. \nWe also develop a model to incorporate the stochastic asymmetric nonlinear weight update in online (in-situ) training. We propose two solutions for this problem; 1) a compensation technique which is tested on a small scale problem to separate two Laplacian mixed sources using online independent component analysis. 2) stochastic rounding and is tested on a spiking neural network with deep local learning dynamics showing only a 1~2% drop in the baseline accuracy for three different RRAM devices. We also propose Error-triggered learning to overcome the limited endurance problem with only 0.3% and 3% drop in the accuracy for N-MNIST and DVSGesture datasets with around 33X and 100X reduction in the number of writes\, respectively. \nFinally\, the prospects of this neuromorphic hardware are discussed to develop new algorithms with the existing resistive crossbar hardware including its nonidealities.
URL:https://www.cecs.uci.edu/event/efficient-offline-and-online-training-of-memristive-neuromorphic-hardware/
LOCATION:CA
CATEGORIES:PhD Defenses
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20191011T150000
DTEND;TZID=America/Los_Angeles:20191011T160000
DTSTAMP:20231011T190904Z
CREATED:20231011T190900Z
LAST-MODIFIED:20231011T190904Z
UID:14567-1570806000-1570809600@www.cecs.uci.edu
SUMMARY:Towards Engineering Computer Vision Systems: From Web to FPGAs
DESCRIPTION:Final Defense – Sajjad Taheri \n\n\nDate: August 26\, 2019\nTime: 2:00 pm \n\nLocation: Donald Bren Hall 4011 \n\n\nCommittee: Alex Nicolau(chair)\, Alex Veidenbaum(co-chair)\, Nikil Dutt \n\n\nTitle: Towards Engineering Computer Vision Systems: From Web to FPGAs\n\n\n\nAbstract:\n\nComputer vision has many applications that impact our daily lives\, such as automation\, entertainment\, healthcare\, etc. However\, computer vision is very challenging. This is in part due to intrinsically difficult nature of the problem and partly due to the complexity and size of visual data that need to be processed. To be able to deploy computer vision in many practical use cases\, sophisticated algorithms and efficient implementations are required. In this dissertation\, we consider two platforms that are suitable for computer vision processing\, yet they were not easily accessible to algorithm designers and developers: The Web and FPGA-based Accelerators. Through the development of open-source software\, we highlight challenges associated with vision development on each platform and demonstrate opportunities to mitigate them.\n\nThe Web is the world’s most ubiquitous computing platform which hosts a plethora of visual content. Due to historical reasons such as insufficient JavaScript performance and lack of API support for acquiring and manipulating images\, computer vision is not mainstream on the Web. We show that in light of recent web developments such as vastly improved JavaScript performance and addition of APIs such as WebRTC\, efficient computer vision processing can be realized on web clients. Through novel engineering techniques\, we translate a popular open-source computer vision library (OpenCV) from C++ to JavaScript and optimize its performance for the web environment. We demonstrate that hundreds of computer vision functions run in browsers with performance close to their original C++ version.\n\nField Programmable Gate Arrays (FPGA)s are a promising solution to mitigate the computational cost of vision algorithms through hardware pipelining and parallelism while providing excellent power efficiency. However\, an efficient FPGA implementation of vision algorithm requires hardware design expertise and a considerable amount of engineering person-hours. We show how high-level graph-based specifications\, such as OpenVX can significantly improve FPGA design productivity. Since such abstractions exclude implementation details\, different implementation configurations that satisfy various design constraints\, such as performance and power consumption\, can be explored systematically. They also enable a variety of local and global optimizations to apply across the algorithms.
URL:https://www.cecs.uci.edu/event/towards-engineering-computer-vision-systems-from-web-to-fpgas/
LOCATION:CA
CATEGORIES:PhD Defenses
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20190826T150000
DTEND;TZID=America/Los_Angeles:20190826T160000
DTSTAMP:20231011T190904Z
CREATED:20231011T190859Z
LAST-MODIFIED:20231011T190904Z
UID:14565-1566831600-1566835200@www.cecs.uci.edu
SUMMARY:Event Detection and Estimation Using Distributed Population Owned Sensors
DESCRIPTION:Name: Ahmed Mokhtar Nagy Ibrahim\nChair: Prof. Ahmed Eltawil \nDate: August 7th\, 2019 \nTime: 1:00 pm \nLocation:  EH 2210 – EECS Chair’s Conference Room \nCommittee: Ahmed Eltawil(Chair)\, Ender Ayanoglu\, and Lee Swindlehurst \nTitle:”Event Detection and Estimation Using Distributed Population Owned Sensors ” \nAbstract:\nSmart phones are an indispensable tool in modern day-to-day life. Their widespread use has spawned numerous applications targeting diverse domains such as bio-medical\, environment sensing and infrastructure monitoring. In such applications\, the accuracy of the sensors at the core of the system is still questionable\, since these devices are not originally designed for high accuracy sensing purposes. In this thesis\, we investigate the accuracy limits of one of the commonly used sensors\, namely\, a smart phone accelerometer. As a use case\, we focus on utilizing smart phone accelerometers in structural health monitoring (SHM). Using the already deployed network of distributed citizen-owned sensors is considered a cheap alternative to standalone sensors. These devices can capture floors vibration during disasters\, and consequently compute the instantaneous displacement of each floor. Hence\, damage indicators defined by government standards such as peak relative displacement can be estimated. In this work\, we study the displacement estimation accuracy and propose a zero-velocity update (ZUPT) method for noise cancellation. Theoretical derivation and experimental validation are presented\, and we discuss the impact of sensor error on the achieved building classification accuracy. Moreover\, in spite of the presence of sensor error\, SHM systems can be resilient by adopting machine learning. Several algorithms such as support vector machine (SVM)\, K-nearest neighbor (KNN) and convolutional neural network (CNN) are adopted and compared. Techniques for addressing noise levels are proposed and the results are compared to regular noise cancellation techniques such as filtering. \nFinally\, since most previous work focused on modelling the sensor chip error itself\, we study other sources of error such as sampling time uncertainty which is introduced by the device operating system (OS). That type of error can be considered a major contributor to the overall error\, specially for sufficiently large signals. Hence\, we propose a novel smart device accelerometer error model that includes the traditional additive noise as well as sampling time uncertainty errors. The model is validated experimentally using shake table experiments\, and maximum likely-hood estimation (MLE) is used to estimate the model parameters. Moreover\, we derive the Cramer-Rao lower bound (CRLB) of acceleration estimation based on the proposed model.
URL:https://www.cecs.uci.edu/event/event-detection-and-estimation-using-distributed-population-owned-sensors/
LOCATION:CA
CATEGORIES:PhD Defenses
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20190812T150000
DTEND;TZID=America/Los_Angeles:20190812T160000
DTSTAMP:20231011T190852Z
CREATED:20231011T190846Z
LAST-MODIFIED:20231011T190852Z
UID:14559-1565622000-1565625600@www.cecs.uci.edu
SUMMARY:Cooperative Power and Resource Management for Heterogeneous Mobile Architectures
DESCRIPTION:Name: Chenying Hsieh\n\nDate: Wednesday Aug 7\, 2019 \n\n\n\n\n\n\nTime: 2:00 pm\n\nLocation: Donald Bren Hall 4011\n\nCommittee: Nikil Dutt (Chair)\, Tony Givargis\, and Ardalan Amiri Sani\n\nTitle: Cooperative Power and Resource Management for Heterogeneous Mobile Architectures\n \nAbstract:\n\n\n\n\n\n\nHeterogeneous architectures have been ubiquitous in mobile system-on-chips (SoCs). The demand from different application domains such as games\, computer vision and machine learning which requires massive parallelism of computation has driven the integration of more accelerators into mobile SoCs to provide satisfactory performance energy-efficiently.These on-chip computing resources typically have their individual runtime systems including: (1) a software governor: continuously monitors hardware utilization and makes decisions of trade-off between performance and power consumption. (2) software stack: allows application developers to program the hardware for general purpose computation and perform memory management and profiling. As computation of mobile applications may demand all sorts of combinations of computing resources\, we identify two problems: (1) individual runtime can often lead to poor performance-power trade-off or inefficient utilization of computing resources. (2) existing approaches fail to schedule subprograms among different computing resources and further lose the opportunity to avoid resource contention to gain better performance.\n\nTo address these issues\, we propose a holistic approach to coordinate different runtime regrading application performance and energy efficiency in this dissertation. We first present MEMCOP\, a memory-aware collaborative CPU-GPU governor that considers both the memory access footprint as well as the CPU/GPU frequency to improve energy efficiency of high-end mobile game workloads by performing dynamic voltage and frequency scaling (DVFS). Second\, We present a case study executing a mix of popular data-parallel workloads such as convolutional neural networks (CNNs)\, computer vision filters and graphics rendering kernels on mobile devices\, and show that both performance and energy consumption of mobile platforms can be improved by synergistically deploying these underutilized compute resources. Third\, we present SURF: a self-aware unified runtime framework for parallel programs on heterogeneous mobile architectures. SURF supports several heterogeneous parallel programming languages (including OpenMP and OpenCL)\, and enables dynamic task-mapping to heterogeneous resources based on runtime measurement and prediction.The measurement and monitoring loop enables self-aware adaptation of run-time mapping to exploit the best available resource dynamically.\n\nWe implemented all the software components on real-world mobile SoCs and evaluate our proposed approaches with mobile games and mix of parallel benchmarks and CNN applications accordingly.
URL:https://www.cecs.uci.edu/event/cooperative-power-and-resource-management-for-heterogeneous-mobile-architectures/
LOCATION:CA
CATEGORIES:PhD Defenses
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20190806T150000
DTEND;TZID=America/Los_Angeles:20190806T160000
DTSTAMP:20231011T190852Z
CREATED:20231011T190846Z
LAST-MODIFIED:20231011T190852Z
UID:14558-1565103600-1565107200@www.cecs.uci.edu
SUMMARY:Low-Power Integrated Circuits For Biomedical Applications
DESCRIPTION:Title: “Low-Power Integrated Circuits For Biomedical Applications”\nName: Karimi Bidhendi\, Alireza \nChair: Professor Payam Heydari \nDate: August 6\, 2019 \nTime: 1 pm \nLocation: Calit2\,  Room 3008 \nAbstract: \nWith thousands new cases of spinal cord injury reported everyday\, many people suffer from paralysis and loss of sensation in both legs. Beside the healthcare costs\, such a state severely deteriorates a patients quality of life and may even lead to additional medical conditions. Therefore\, there is a growing need for cyber-physical systems to restore the walking ability through bypassing the damaged spinal cord. This goal can be achieved by monitoring and processing patient’s brain signals to enable brain-directed control of prosthetic legs. Among several existing methods to record brain signals\, electrocorticography (ECoG) has gained popularity due to being robust to motion artifacts\, having high spatial resolution and signal to noise ratio\, being moderately invasive and the possibility of chronic implantation of recording grids with no or minor scar tissue formation. The latest property is of particular importance for the whole system to be a viable fully implantable solution. Furthermore\, the implanted system has to operate independently with no or minimal need of external hardware (e.g. a bulky personal computer) to be individually and socially accepted. \nTo implement a fully implantable system\, low-power and miniaturized electronics are needed to reduce heat generation\, increase battery life-time and be minimally intrusive. These requirements indicate that many of the system’s components should be custom-designed to integrated as much functionality as possible in a given real estate. This thesis presents silicon tested prototypes of several building blocks for the envisioned system\, namely\, ultra low-power brain signal acquisition front-ends\, a low-power and inductorless MedRadio transceiver\, and a fast start-up crystal oscillator. Brain signal acquisition front-ends provide low noise amplification of weak ECoG biosignals. MedRadio transceiver enables communication between the implant and end effectors or base station (e.g. prosthetic legs or desktop computer). Crystal oscillators generate the reference signal for other system components such as analog to digital converters. Novel techniques to improve important performance parameters (power consumption\, low noise operation and interference resilience) have been introduced. Electrical\, in-vitro and in-vivo experimental measurements have verified the functionality and performance of each design.
URL:https://www.cecs.uci.edu/event/low-power-integrated-circuits-for-biomedical-applications/
LOCATION:CA
CATEGORIES:PhD Defenses
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20190522T150000
DTEND;TZID=America/Los_Angeles:20190522T160000
DTSTAMP:20231011T190852Z
CREATED:20231011T190846Z
LAST-MODIFIED:20231011T190852Z
UID:14556-1558537200-1558540800@www.cecs.uci.edu
SUMMARY:Data-Driven Modeling of Cyber-Physical Systems using Side-Channel Analysis
DESCRIPTION:Title: “Data-Driven Modeling of Cyber-Physical Systems using Side-Channel Analysis”\nName: Sujit Rokka Chhetri \nDate: May 20\, 2019 \nTime: 3:00 PM \nLocation: EH 5200 \nCommittee: Prof. Mohammad Al Faruque (Chair)\, Prof. Pramod Khargonekar\, Prof. Fadi Kurdahi \nAbstract: \nCyber-Physical System consists of the integration of computational components in the cyber-domain with the physical-domain processes. In cyber-domain\, embedded computers and networks monitor and control the physical processes\, and in the physical-domain the sensors and actuators aid in interacting with the physical world. This interaction between the cyber and physical domain brings unique modeling challenges one of which includes the integration of discrete models in cyber-domain with the continuous physical domain processes. However\, the same cyber-physical interaction also opens new opportunities for modeling. For example\, the information flow in the cyber-domain manifests physically in the form of energy flows in the physical domain. Some of these energy flows unintentionally provide information about the cyber-domain through the side-channels. \nIn this thesis\, the extensive analysis of the side-channels (such as acoustic\, magnetic\, thermal\, power and vibration) of the cyber-physical system is performed. Based on this analysis data-driven models are estimated. These models are then used to perform security vulnerability analysis (for confidentiality and integrity)\, whereby\, new attack models are explored.  Furthermore\, the data-driven models are also utilized to create a defensive mechanism to minimize the information leakage from the system and to detect attacks to the integrity of the system. The cyber-physical manufacturing systems are taken as use cases to demonstrate the applicability of the modeling approaches. \nFinally\, the side-channel analysis is also performed to aid in modeling digital twins of the cyber-physical systems. Specifically\, a novel methodology to utilize low-end sensors to analyze the side-channels and build the digital twins is proposed. These digital twins are used to capture the interaction between the cyber-domain and the physical domain of the manufacturing systems\, and aid in process quality inference and fault localization. Using side-channels these digital twins are kept up-to-date\, which is one of the fundamental requirements for building digital twins.
URL:https://www.cecs.uci.edu/event/data-driven-modeling-of-cyber-physical-systems-using-side-channel-analysis/
LOCATION:CA
CATEGORIES:PhD Defenses
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20190227T150000
DTEND;TZID=America/Los_Angeles:20190227T160000
DTSTAMP:20231011T190843Z
CREATED:20231011T190828Z
LAST-MODIFIED:20231011T190843Z
UID:14548-1551279600-1551283200@www.cecs.uci.edu
SUMMARY:Cyber-Physical Systems Approach to Irrigation Systems
DESCRIPTION:Title: “Cyber-Physical Systems Approach to Irrigation Systems”\nName: Davit Hovhannisyan \nDate: March 5\, 2019 \nTime: 4:00PM \nLocation: EH 3206  (CECS conference room) \nCommittee:  Prof. Fadi Kurdahi (Chair)\, Prof. Ahmed Eltawil\, Prof. Mohammad Al Faruque \nAbstract: \nSemiconductor industry has successfully brought silicon technology to price point that it is accessible for application domains such as irrigation systems\, which wastefully utilizes 70% of all fresh water. Moreover\, worldwide fresh water resources will soon reach a deficit due to ever growing demand\, while the state of the art precision irrigation systems utilize sophisticated water delivery drip lines\, yet are only controlled at source by the gut of the end user. This work demonstrates that scientific foundation of cyber-physical systems can be used to design automated\, distributed and intelligent precision irrigation systems that improve irrigation efficiency. Thus\, this work explores and analyzes in depth the cross section of irrigation practices and cyber-physical systems knowledge to show a path toward a successful adaptation of silicon technology that solves one of the greatest challenges of the 21st century\, the fresh water scarcity. To that end\, this work presents contributions that complete a novel vision for next generation precision irrigation systems\, which can be grouped into three main thrusts: (1) circuit inspired models for irrigation system components and scheduling strategies by analogy method\, (2) CPS approach based (a) design methodology capable of comparing irrigation controllers\, (b) simulation tools and software for analyzing the distributed behavior of the specialized irrigation controllers\, (c) topology adaptation technique that utilizes multi-graphs to mine the hydro-wireless topology of the IoT controllers\, and (d) a distributed controller implementation with novel energy harvesting and low power support for irrigation controllers and sensors\, (3) overhead vision solutions for health and growth monitoring. The observations\, analysis and insight from experimental studies were in collaboration with Rancho California Water District\, growers and practitioners.
URL:https://www.cecs.uci.edu/event/cyber-physical-systems-approach-to-irrigation-systems/
LOCATION:CA
CATEGORIES:PhD Defenses
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20181128T150000
DTEND;TZID=America/Los_Angeles:20181128T160000
DTSTAMP:20231011T190843Z
CREATED:20231011T190827Z
LAST-MODIFIED:20231011T190843Z
UID:14546-1543417200-1543420800@www.cecs.uci.edu
SUMMARY:Control System Design Automation Using Reinforcement Learning
DESCRIPTION:Title: “Control System Design Automation Using Reinforcement Learning”\nName: Hamid Mirzaei \nDate: Tuesday\, November 20\, 2018 \nTime: 1:00 p.m. \nLocation: Donald Bren Hall 3011 \nCommittee: Professor Tony Givargis (Chair)\, Professor Eli Bozorgzadeh\, Professor Ian Harris \nAbstract: \n\n\n\n\n\n\n\n\n\nConventional control theory has been used in many application domains with great success in the past decades. However\, novel solutions are required to cope with the challenges arising from complex interaction of fast growing cyber and physical systems. Specifically\, integration of classical control methods with Cyber-Physical System (CPS) design tools is a non-trivial task since those methods have been developed to be used by human expert and are not intended to be part of an automatic design tool. \n\n\n\n\n\n\n\n\n\n\n\n\nOn the other hand\, the control problems in emerging Cyber-Physical Systems\, such as intelligent transportation and autonomous driving\, cannot be addressed by conventional control methods due to the high level of uncertainty\, complex dynamic model requirements and operational and safety constraints.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nIn this dissertation\, a holistic CPS design approach is proposed in which the control algorithm is incorporated as a building block in the design tool. The proposed approach facilitates the inclusion of physical variability into the design process and reduces the parameter space to be explored. This has been done by adding constraints imposed by the control algorithm.\n\n\nFurthermore\, Reinforcement Learning (RL) as a replacement for convection control solutions are studied in the emerging domain of intelligent transportation systems. Specifically\, dynamic tolling assignments and autonomous intersection management are tackled by the state-of-the-art RL methods\, namely\, Trust Region Policy Optimization and Finite-Difference Gradient Descent. Additionally\, Q-learning is used to improve the performance of an embedded controller using a novel formulation in which cyber-system actions\, such as changing control sampling time\, is combined with the physical action set of the RL agent. Using the proposed approach\, it is shown that the power consumption and computational overhead of the embedded control can be improved.\n\nFinally\, to address the current lack of available physical benchmarks\, an open physical environment benchmarking framework is introduced. In the proposed framework\, various components of a physical environment are captured in a unified repository to enable researchers to define and share standard benchmarks that can be used to evaluate different reinforcement algorithms. They can also share the realized environments via the cloud to enable other groups perform experiments on the actual physical environments instead of currently available simulation-based environments.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n 
URL:https://www.cecs.uci.edu/event/control-system-design-automation-using-reinforcement-learning/
LOCATION:CA
CATEGORIES:PhD Defenses
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20181016T150000
DTEND;TZID=America/Los_Angeles:20181016T160000
DTSTAMP:20231011T190904Z
CREATED:20231011T190859Z
LAST-MODIFIED:20231011T190904Z
UID:14566-1539702000-1539705600@www.cecs.uci.edu
SUMMARY:An Initial Study of Simple Approaches To Eliminating Out-of-Thin-Air Results
DESCRIPTION:Name: Peizhao Ou\nChair: Prof. Brian Demsky \nDate: Tuesday October 16\, 2018 \nTime: 10am – 12pm \nLocation: CALIT2 Room 3008 \nTitle: An Initial Study of Simple Approaches To Eliminating Out-of-Thin-Air Results \nAbstract:\nEliminating so-called “out-of-thin-air” (OOTA) results is an open problem in many existing programming language memory models including Java\, C\, and C++.  OOTA behaviors are problematic in that they break both formal and informal modular reasoning about program behavior. In spite of many years of research\nefforts\, defining memory model semantics that are easily understood\, allow existing optimizations\, and forbid OOTA results remains an open problem. This thesis explores two simple solutions to this problem that forbid OOTA results. The first solution is targeted towards Java-like languages in which all memory operations may create OOTA executions\, and the second solution is targeted towards C/C++-like memory models in which racing operations are explicitly labeled as atomic operations. Our solutions provide a per-candidate execution criterion that makes it possible to examine a single execution and determine whether the memory model permits the execution. We implemented and evaluated both solutions in the LLVM compiler framework. Our results show that on an ARMv8 processor the first solution has an average overhead of 3.1% and a maximum overhead of 17.6% on the SPEC CPU2006 C/C++ benchmarks\, and that the second solution has no overhead on average and a maximum overhead of 6.3% on 43 concurrent data structures.  The results indicate that these simple approaches to eliminating out-of-thin-air behaviors deserve further consideration.
URL:https://www.cecs.uci.edu/event/an-initial-study-of-simple-approaches-to-eliminating-out-of-thin-air-results/
LOCATION:CA
CATEGORIES:PhD Defenses
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20180607T150000
DTEND;TZID=America/Los_Angeles:20180607T160000
DTSTAMP:20231011T190742Z
CREATED:20231011T190742Z
LAST-MODIFIED:20231011T190742Z
UID:14530-1528383600-1528387200@www.cecs.uci.edu
SUMMARY:Modeling and Co-Design of Multi-domain Cyber-Physical Systems
DESCRIPTION:Name: Jiang Wan \nDate: Friday\, June 1 \nTime: 3:00 — 5:00 PM \nLocation: EH 3206 \nCommittee: Fadi Kurdahi (Chair)\, Mohammad Al Faruque\, Rainer Doemer \nAbstract: \nCyber-Physical Systems (CPS) are integration of computation and physical processes connected through networks. The high complexity of cross-domain engineering in combination with the pressure for system innovation\, higher quality\, time-to-market\, and budget constraints make it imperative for engineers to use integrated engineering methods and tools for CPS design. However\, existing computer-based engineering tools are mainly focused on a particular domain and therefore it is challenging to perform system-level analysis for CPS due to the difficulty of knowledge integration from different domains. This thesis studies the problem in the modeling of cross-domain CPS. Problems in the modeling of both functional and non-functional requirements during CPS design are explored and a functional model-based approach is proposed for high level CPS modeling. Moreover\, targeting the security requirement in CPS\, which is one of the key non-functional requirements in CPS\, physics-based models and solutions are proposed in this thesis. \n 
URL:https://www.cecs.uci.edu/event/modeling-and-co-design-of-multi-domain-cyber-physical-systems/
LOCATION:CA
CATEGORIES:PhD Defenses
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20180606T150000
DTEND;TZID=America/Los_Angeles:20180606T160000
DTSTAMP:20231011T190742Z
CREATED:20231011T190742Z
LAST-MODIFIED:20231011T190742Z
UID:14529-1528297200-1528300800@www.cecs.uci.edu
SUMMARY:Reliable and Energy Efficient Battery-Powered Cyber-Physical Systems
DESCRIPTION:Name: Korosh Vatanparvar\nDate: Wednesday\, May 30th \nTime: 3:00 — 5:00 PM \nLocation: Calit2 3008 \nCommittee: Professor Prof. Mohammad Al Faruque (Chair) \nAbstract: \nCyber-Physical Systems (CPS) were presented as a solution to multidisciplinary integration and control in embedded systems. They provide seamless interactions between cyber and physical domains\, enabling more intelligent and complicated control applications. However\, CPS face the challenges of reliability and energy efficiency since they mainly rely on batteries for power supply. \nWe investigate these issues with Electric Vehicles (EV) which are common battery-powered CPS. EV were introduced as a mean of transportation to address environmental problems like air and noise pollution. However\, their stringent design constraints\, especially on battery packs\, create challenges of limited driving range and battery lifetime for daily drivers and manufacturers. Design automation community has been addressing these by developing more efficient and dependable devices and control methodologies. Our contributions in this thesis will embrace: \n1) novel machine learning and physics-based modeling techniques to capture CPS dynamics more accurately; 2) unique optimization problem formulations to make optimal control decisions; and 3)intelligent control methodologies that leverage the modeling and interaction within CPS to achieve reliable and efficient operation. These contributions are applied to the systems in EV such as navigation system\, climate control\, and battery management system. Our objectives are to further extend the EV driving range and prolong the battery lifetime while maintaining similar driving experience and comfort for passengers. \n 
URL:https://www.cecs.uci.edu/event/reliable-and-energy-efficient-battery-powered-cyber-physical-systems/
LOCATION:CA
CATEGORIES:PhD Defenses
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20180517T150000
DTEND;TZID=America/Los_Angeles:20180517T160000
DTSTAMP:20231011T190732Z
CREATED:20231011T190732Z
LAST-MODIFIED:20231011T190732Z
UID:14526-1526569200-1526572800@www.cecs.uci.edu
SUMMARY:Reflective On-Chip Resource Management Policies for Energy-Efficient Heterogeneous Multiprocessors
DESCRIPTION:Name: Tiago Mück\nDate: May 16\, 2018 \nTime: 2:00pm \nLocation: Donald Bren Hall 2011 \nCommittee: Nikil Dutt (Chair)\, Alex Nicolau\, Tony Givargis \nAbstract: \nEffective exploitation of power-performance tradeoffs in heterogeneous many-core platforms (HMPs)\, requires intelligent on-chip resource management at different layers\, in particular at the operating system level. Operating systems need to continuously analyze the application behavior and find a proper answer for questions such as: What is the most power efficient core type to execute the application without violating its performance requirements? or Which option is more power-efficient for the current application: an out-of-order core at a lower frequency or an inorder core at a higher frequency?\nUnfortunately\, existing operating systems (e.g. Linux) do not offer mechanisms to properly address these questions and therefore are unable to fully exploit architectural heterogeneity for scalable energy-efficient execution of dynamic workloads.\nThis dissertation proposes a holistic approach for performing resource allocation decisions and power management by leveraging concepts from reflective software.\n\nThe general idea of reflection is to change your actions based on both external feedback and introspection (i.e.\, self-assessment).\n\n\nFrom a practical computer system perspective\, reflection means performing resource management actions considering both sensing information (e.g.\, readings from performance counters\, power sensors\, etc.) to assess the current system state\, as well as models to predict the behavior of the system before performing an action.\nIn this context\, this dissertation describes MARS\, a Middleware for Adaptive Reflective computer Systems. MARS consists of a framework and a set of models for creating reflective resource managers. MARS is implemented and evaluated on top a real Linux-based platform. Furthermore\, MARS also provides an offline simulation infrastructure for fast prototyping of policies and large-scale or long-term policy evaluation.\n\nExperimental evaluation shows that MARS’s models allow different policies for task mapping and dynamic voltage scaling to be seamlessly integrated\, resulting in up-to 1.8x energy efficiency improvements without performance degradation when compared to vanilla Linux.
URL:https://www.cecs.uci.edu/event/reflective-on-chip-resource-management-policies-for-energy-efficient-heterogeneous-multiprocessors/
LOCATION:CA
CATEGORIES:PhD Defenses
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20180516T150000
DTEND;TZID=America/Los_Angeles:20180516T160000
DTSTAMP:20231011T190732Z
CREATED:20231011T190732Z
LAST-MODIFIED:20231011T190732Z
UID:14525-1526482800-1526486400@www.cecs.uci.edu
SUMMARY:SIMD Assisted Fault Detection and Fault Attack Mitigation
DESCRIPTION:Name: Zhi Chen\nDate: May 15\, 2018 \nTime: 2:30pm \nLocation: Donald Bren Hall 3011 \nCommittee: Professor Alex Nicolau (Chair)\, Alex Veidenbaum\, Nikil Dutt \nAbstract: \nModern processors continue to aggressively scale down the feature size and reduce voltage levels to run faster and be more energy efficient. However\, this trend also poses significant reliability concern as it makes transistors more susceptible to soft errors. Soft errors are transient. Although they don’t impair the computing systems permanently\, these errors can corrupt the output of a program or even crash the entire system. Hardware or software redundant techniques could be used to detect errors during the execution of a program. However\, hardware redundancy\, e.g. DMR (dual-modular redundancy) and TMR (triple-modular redundancy)\, leads to significant area overhead and very high energy cost. Software redundancy\, e.g. instruction duplication\, has lower performance and energy penalty and  virtually no hardware cost by sacrificing a small degree of error coverage. Yet commodity processors generally don’t require “five-nines” reliability as they are not mission-critical. Instead\, performance and energy consumption have more priority. This dissertation proposes a novel approach to instruction duplication\, which exploits the redundancy within SIMD instructions. The key idea is to pack the original data and its duplicate in the different lanes of the same vector register instead of executing two scalar instructions separately as these registers are underutilized on most applications. The proposed solution is implemented in the LLVM compiler as a stand-alone pass. Evaluation on a host of benchmarks reveal that proposed SIMD-based error detection technique causes much less performance\, code size\, and energy overheads.\n\nThis dissertation further extends the proposed approach as a countermeasure to protect cryptographic algorithms. These algorithms are widely adopted in modern processors and embedded systems to protect information. A number of popular cryptographic algorithms in the Libgcrypt library are protected using the SIMD-based instruction duplication technique. A large amount of errors are injected to these algorithms. The results show that almost all injected faults can be detected with reasonable performance and code size cost.\n 
URL:https://www.cecs.uci.edu/event/simd-assisted-fault-detection-and-fault-attack-mitigation/
LOCATION:CA
CATEGORIES:PhD Defenses
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20180503T150000
DTEND;TZID=America/Los_Angeles:20180503T160000
DTSTAMP:20231011T190730Z
CREATED:20231011T190725Z
LAST-MODIFIED:20231011T190730Z
UID:14517-1525359600-1525363200@www.cecs.uci.edu
SUMMARY:"Efficient Acceleration of Computation Using Associative In-memory Processing"
DESCRIPTION:Title: “Efficient Acceleration of Computation Using Associative In-memory Processing”\nSpeaker: Hasan Erdem Yantir\, University of California\, Irvine \nDate and Time: Monday\, May 14\, 2018 at 9:00AM \nLocation: Engineering Hall 3206 \nCommittee: Professor Fadi Kurdahi (Chair)\, Ahmed Eltawil\, Rainer Doemer \nAbstract: \nThe complexity of the computational problems is rising faster than the computational platforms’ capabilities. This forces researchers to find alternative paradigms and methods for efficient computing. One promising paradigm is accelerating compute-intensive kernels using in-memory computing accelerators since memory is the major bottleneck that limits the amount of parallelism and performance of a system and dominates energy consumption in computation. Leveraging the memory intensive nature of big data applications\, an in-memory-based computation system can be presented where logic can be replaced by memory structures\, virtually eliminating the need for memory load/store operations during computation. The massive parallelism enabled by such a paradigm results in highly scalable structures. \nThe present thesis is studied against this background. The objective is to conduct a broad perspective research on in-memory computing. For this purpose\, associative computing architectures (i.e.\, Associative Processors\, or AP) are built by both traditional (SRAM) and emerging (ReRAM) memory technologies together with their corresponding software frameworks. For ReRAM-based APs\, the reliability concerns coming with the emerging memories are resolved. Architectural innovations are developed to increase the energy efficiency. Furthermore\, approximate computing approach is introduced for APs to perform efficient/low-power approximate in-memory computing for the tasks which can tolerate some accuracy lost.  The works also propose a novel two-dimensional in-memory computing architecture to cope with the existing deficiencies of the traditional one-dimensional AP architectures.
URL:https://www.cecs.uci.edu/event/efficient-acceleration-of-computation-using-associative-in-memory-processing/
LOCATION:CA
CATEGORIES:PhD Defenses
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20180411T150000
DTEND;TZID=America/Los_Angeles:20180411T160000
DTSTAMP:20231011T190729Z
CREATED:20231011T190725Z
LAST-MODIFIED:20231011T190729Z
UID:14516-1523458800-1523462400@www.cecs.uci.edu
SUMMARY:"A Compiler Infrastructure for Static and Hybrid Analysis of Discrete Event System Models"
DESCRIPTION:Title: “A Compiler Infrastructure for Static and Hybrid Analysis of Discrete Event System Models”\nSpeaker: Tim Schmidt\, University of California\, Irvine \nDate and Time: Friday\, April 20\, 2018 at 10:00AM-11:00PM \nLocation: Engineering Hall 3206 \nCommittee: Professor Rainer Doemer (Chair)\, Fadi Kurdahi\, Kwei-Jay Lin \nAbstract: \nThe design of embedded systems is a well-established research domain for many decades. However\, the constantly increasing complexity and requirements of state-of-the-art embed- ded systems pushes designers to new challenges while maintaining established design methodologies. Embedded system design uses the concept of Discrete Event Simulation (DES) to prototype and test the interaction of individual components. \nIn this dissertation\, we provide the Recoding Infrastructure for SystemC (RISC) compiler framework to perform static and hybrid analysis of IEEE SystemC models. On one hand\, RISC generates thread communication charts to visualize the communication between individual design components. The\nvisualization respects the underlying discrete event simulation semantics and illustrates the individual synchronization steps. On the other hand\, RISC translates a sequential model into a parallel model\nwhich effectively utilizes multi- and many-core host simulation platforms for faster simulation. This work extends the conflict analysis capabilities for libraries\, dynamic memory allocation\, channel instance awareness\, and references. Additionally\, the traditional thread level parallelism is extended with data level parallelism for even faster simulation.
URL:https://www.cecs.uci.edu/event/a-compiler-infrastructure-for-static-and-hybrid-analysis-of-discrete-event-system-models/
LOCATION:CA
CATEGORIES:PhD Defenses
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