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DTSTART;TZID=America/Los_Angeles:20190129T150000
DTEND;TZID=America/Los_Angeles:20190129T160000
DTSTAMP:20231011T190843Z
CREATED:20231011T190827Z
LAST-MODIFIED:20231011T190843Z
UID:14547-1548774000-1548777600@www.cecs.uci.edu
SUMMARY: Partial logic synthesis and its application to automatic generation of parallel/distributed algorithms
DESCRIPTION:Name: Masahiro Fujita\nDate and Time: Thursday\, January 14 at 3:00 p.m. \nLocation:Engineering Hall 3206 \nAbstract: In this talk first we review the partial logic synthesis problem and its associated\nalgorithm based on QBF (Quantified Boolean Formula) formulation. Partial logic synthesis\nis to generate appropriate sub-circuits for the missing portions in the target design so that\nthe entire circuit becomes equivalent to the specification which is separately given. Then\nwe show the problem to automatically generate distributed/parallel computing for the\ngiven specification can be defined as partial logic synthesis problem. Taking matrix-vector\nproduct computation as an example\, we show how theoretical optimum distributed/parallel\ncomputation can be automatically generated targeting many cores/chips which are\nconnected through a ring connection. \nBiography: Masahiro Fujita receivedh is Ph.D. in Information Engineering from the\nUniversity of Tokyo in 1985 on his work on model checking of hardware designs by using\nlogic programming languages. In 1985\, he joined Fujitsu as a researcher and started to\nwork on hardware automatic synthesis as well as formal verification methods and tools\,\nincluding enhancements of BDD/SAT-based techniques. Since March 2000\, he has been a\nprofessor at VLSI Design and Education Center of the University of Tokyo. He has been\ninvolved in a Japanese governmental research project for dependable system designs and\nhas developed a formal verifier for C programs that could be used for both hardware and\nembedded software designs. He has authored and co-authored 10 books\, and has more\nthan 200 publications.
URL:https://www.cecs.uci.edu/event/partial-logic-synthesis-and-its-application-to-automatic-generation-of-parallel-distributed-algorithms/
LOCATION:CA
CATEGORIES:Seminars at CECS
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:20190307T150000
DTEND;TZID=America/Los_Angeles:20190307T160000
DTSTAMP:20231011T190843Z
CREATED:20231011T190828Z
LAST-MODIFIED:20231011T190843Z
UID:14549-1551970800-1551974400@www.cecs.uci.edu
SUMMARY:"Poly-Logarithmic Side Channel Rank Estimation via Exponential Sampling"
DESCRIPTION:Name: Liron David\nDate and Time: Tuesday\, March 12 at 10:00 a.m. \nLocation: Engineering Hall 5204 \nAbstract: \nRank estimation is an important tool for a side-channel evaluations laboratories. It allows estimating the remaining security after an attack has been performed\, quantified as the time complexity and the memory consumption required to brute force the key given the leakages as probability distributions over d subkeys (usually key bytes). These estimations are particularly useful where the key is not reachable with exhaustive search. \nWe propose ESrank\, the first rank estimation algorithm that enjoys provable poly-logarithmic time- and space-complexity\, which also achieves excellent practical performance. Our main idea is to use exponential sampling to drastically reduce the algorithm’s complexity. Importantly\, ESrank is simple to build from scratch\, and requires no algorithmic tools beyond a sorting function. After rigorously bounding the accuracy\, time and space complexities\, we evaluated the performance of ESrank on a real SCA data corpus\, and compared it to the currently-best histogram-based algorithm. We show that ESrank gives excellent rank estimation (with roughly a 1-bit margin between lower and upper bounds)\, with a performance that is on-par with the Histogram algorithm: a run-time of under 1 second on a standard laptop using 6.5 MB RAM. \nBiography: \nLiron David is a Ph.D. candidate in Electrical Engineering at Tel-Aviv University under the supervision of Prof. Avishai Wool. She received her B.Sc. degree in Computer Science and Electrical and Electronics Engineering from Tel-Aviv University and her M.Sc. degree in Electrical Engineering from Tel-Aviv University. Liron has won the Weinstein award for excellence in studies in 2017 \,the Weinstein best paper prize in 2018 and the Tel-Aviv University excellence in teaching in 2018.
URL:https://www.cecs.uci.edu/event/poly-logarithmic-side-channel-rank-estimation-via-exponential-sampling/
LOCATION:CA
CATEGORIES:Seminars at CECS
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20190501T150000
DTEND;TZID=America/Los_Angeles:20190501T160000
DTSTAMP:20231011T190852Z
CREATED:20231011T190846Z
LAST-MODIFIED:20231011T190852Z
UID:14555-1556722800-1556726400@www.cecs.uci.edu
SUMMARY:"Safety Verification and Training for Learning-enabled Cyber-Physical Systems"
DESCRIPTION:Name: Jyotirmoy Vinay Deshmukh\nDate and Time: Tuesday\, May 14\, 2019 at 2:00 p.m. \nLocation: Donald Bren Hall 4011 \nAbstract: \nWith the increasing popularity of deep learning\, there have been several efforts to use neural network based controllers in cyber-physical system applications. However\, neural networks are equally well-known for their lack of interpretability\, explainability and verifiability. This is especially an issue for safety-critical cyber-physical systems such as unmanned aerial vehicles or autonomous ground vehicles. How can we verify that a neural network based controller will always keep the system safe? We look at a new verification approach based on automatically synthesizing a barrier certificate for the system to prove that: starting from a given set of initial conditions\, the system behavior can never reach an unsafe state. Barrier Certificates are essentially a generalization of inductive invariants for continuous dynamical systems\, and we will show how we can use nonlinear SMT solvers to establish the barrier certificate conditions. A more intriguing challenge is whether we can actually train neural networks to obey safety constraints. We will look at a new way of reward shaping in reinforcement learning that could help achieve this goal. \nBiography: \nJyotirmoy V. Deshmukh (Jyo) is an assistant professor in the Department of Computer Science in the Viterbi School of Engineering at the University of Southern California in Los Angeles\, USA. Before joining USC\, Jyo worked as a Principal Research Engineer in Toyota Motors North America R&D. He got his Ph.D. degree from the University of Texas at Austin and was a post-doctoral fellow at the University of Pennsylvania. Jyo’s research interest is in the broad area of formal methods. Currently\, Jyo is interested in using logic-based methods for machine learning\, and in techniques for the analysis\, design\, verification and synthesis of cyber-physical systems\, especially those that use AI-based perception\, control and planning algorithms.
URL:https://www.cecs.uci.edu/event/safety-verification-and-training-for-learning-enabled-cyber-physical-systems/
LOCATION:CA
CATEGORIES:Seminars at CECS
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:20190703T150000
DTEND;TZID=America/Los_Angeles:20190703T160000
DTSTAMP:20231011T190852Z
CREATED:20231011T190846Z
LAST-MODIFIED:20231011T190852Z
UID:14557-1562166000-1562169600@www.cecs.uci.edu
SUMMARY:"Scalable Set-based Analysis for Verification of Cyber-Physical Systems”
DESCRIPTION:Name: Stanley Bak\nDate and Time: Tuesday\, July 9\, 2019 \nLocation: Engineering Hall 2430 \nAbstract: \nCyber-physical systems combine complex physics with complex software. Although these systems offer significant potential in fields such as smart grid design\, autonomous robotics and medical systems\, verification of CPS designs remains challenging. Model-based design permits simulations to be used to explore potential system behaviors\, but individual simulations do not provide full coverage of what the system can do. In particular\, simulations cannot guarantee the absence of unsafe behaviors\, which is unsettling as many CPS are safety-critical systems. \nThe goal of set-based analysis methods is to explore a system’s behaviors using sets of states\, rather than individual states. The usual downside of this approach is that set-based analysis methods are limited in scalability\, working only for very small models. This talk describes our recent process on improving the scalability of set-based reachability computation for LTI hybrid automaton models\, some of which can apply to very large systems (up to one billion continuous state variables!). Lastly\, we’ll discuss the significant overlap of techniques used for our scalable reachability analysis methods with set-based input/output analysis of neural networks. \nBiography: \nStanley Bak is a research computer scientist investigating the formal verification of cyber-physical systems. He strives to create scalable and automatic formal analysis methods for complex models with both ordinary differential equations and discrete behaviors. The ultimate goal is to make formal approaches applicable\, which demands developing new theory\, programming efficient tools and building experimental systems. \nStanley Bak received a Bachelor’s degree in Computer Science from Rensselaer Polytechnic Institute (RPI) in 2007 (summa cum laude)\, a Master’s degree in Computer Science from the University of Illinois at Urbana-Champaign (UIUC) in 2009\, and a PhD from UIUC in 2013. He received the Founders Award of Excellence for his undergraduate research at RPI in 2004\, the Debra and Ira Cohen Graduate Fellowship from UIUC twice\, in 2008 and 2009\, and was awarded the Science\, Mathematics and Research for Transformation (SMART) Scholarship from 2009 to 2013. Stanley worked as a research computer scientist for the Air Force Research Laboratory (AFRL) from 2013 to 2018\, both in the Information Directorate and the Aerospace Systems Directorate. Currently\, he helps run Safe Sky Analytics\, a small research consulting company working with the FAA and the Air Force.
URL:https://www.cecs.uci.edu/event/scalable-set-based-analysis-for-verification-of-cyber-physical-systems/
LOCATION:CA
CATEGORIES:Seminars at CECS
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: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: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: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:20191016T150000
DTEND;TZID=America/Los_Angeles:20191016T160000
DTSTAMP:20231011T190904Z
CREATED:20231011T190900Z
LAST-MODIFIED:20231011T190904Z
UID:14568-1571238000-1571241600@www.cecs.uci.edu
SUMMARY:"Energy-Aware Data Center Management: Monitoring Trends and Insights via Machine Learning"
DESCRIPTION:Name: Hayk Shoukourian\nDate and Time: Friday\, November 15\, 2019 \nLocation: Donald Bren Hall 3011 \nAbstract: \nThe increasing demand for online resources has led to an immense energy burden on contemporary\nHigh Performance Computing (HPC) and cloud data centers. Even though each new generation of HPC systems delivers a higher power efficiency\, the growth in system density and overall performance has continuously contributed to an increase in energy consumption. The mentioned energy consumption not only converts to high operational bills and affects the environment\, but also influences the stability of the underlying power grid. In fact\, all these have already led some governmental organizations to reconsider data center deployment procedures with an increased demand for the renewable energy utilization and waste heat recovery. \nThe talk will give an overview of Leibniz Supercomputing Centre (LRZ)\, introduce its flagship HPC systems\, discuss the high-temperature direct liquid cooling solution and the waste heat reuse. These will be followed by the recent R&D results that rely on ML technologies for forecasting various energy/power consumption relevant Key Performance Indicators at a data center building infrastructure level. The talk will highlight the applications of the developed model outlining its use in proactive management of modern data centers for tackling the above-mentioned challenges. \nBiography: \nDr. Hayk Shoukourian received his M.Sc. and PhD in Computer Science from Technical University of Munich in 2012 and 2015 correspondingly. He joined Leibniz Supercomputing Centre (LRZ) in 2012 and his R&D activities mainly involve efficient energy/power consumption management of the HPC data centers. In his current role\, Dr. Shoukourian is responsible for adaptive modelling of interoperability between the target HPC systems and the building infrastructure of the supercomputing site. He is also a team leader for PRACE (Partnership for Advanced Computing in Europe) work package on “HPC Commissioning and Prototyping”. Since August 2018 Dr. Shoukourian is a lecturer in Computer Science at Ludwig-Maximilians-Universität München (LMU).
URL:https://www.cecs.uci.edu/event/energy-aware-data-center-management-monitoring-trends-and-insights-via-machine-learning/
LOCATION:CA
CATEGORIES:Seminars at CECS
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20191028T150000
DTEND;TZID=America/Los_Angeles:20191028T160000
DTSTAMP:20231011T193349Z
CREATED:20231011T190900Z
LAST-MODIFIED:20231011T193349Z
UID:14569-1572274800-1572278400@www.cecs.uci.edu
SUMMARY:Daniel Gajski and Lukai Cai Win ESWEEK Test of Time Award
DESCRIPTION:Daniel Gajski (Prof. Emeritus) and Lukai Cai (former PhD student\, now at Qualcomm\, San Diego) have been awarded the Embedded Systems Week (ESWEEK) Test of Time Award 2019 for their paper\, “Transaction Level Modeling: An Overview”\, which was originally published at the International Conference on Hardware/Software Codesign and System Synthesis (CODES+ISSS) in 2003.\nWith more than 40 years of intensive engagement in industry and academia and considered on of the founding fathers of new design methods with higher reach towards an upwards degree of abstraction\, Daniel Gajski has been a leader in establishing the fields of silicon compilation\, high-level synthesis and system-level design. \nThis award is in recognition of a paper which has defined a new era in embedded computer system design and is now\, 16 years later\, honored because it has passed the test of time. In the year 2000\, Daniel Gajski’s research group created the concept of Transaction Level Modeling by applying the SpecC methodology used in their “Transaction Level Modeling: An Overview”. This paper marks the milestone of a novel taxonomy where communication and computation are separated in the modeling\, validation\, refinement\, exploration and synthesis of embedded computer systems. Today\, these principles in use throughout national and international academia and industry\, proving the substantial impact their work has created. \nProf. Juergen Teich\, who led the award ceremony together with Prof. Petru Eles (General Chair) in New York on October 16\, emphasized\, “For the first year\, ESWEEK is honoring the authors of one paper with highest impact published at a previous CODES+ISSS\, CASES and EMSOFT conference at ESWEEK with a Test-of-Time Award.” \nWe are proud to see Daniel Gajski and Lukai Cai’s paper receiving this exceptional award.
URL:https://www.cecs.uci.edu/event/daniel-gajski-and-lukai-cai-win-esweek-test-of-time-award/
LOCATION:CA
CATEGORIES:Awards
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20191031T080000
DTEND;TZID=America/Los_Angeles:20191031T170000
DTSTAMP:20231031T183444Z
CREATED:20231031T183444Z
LAST-MODIFIED:20231031T183444Z
UID:15701-1572508800-1572541200@www.cecs.uci.edu
SUMMARY:Rainer Doemer Awarded Dean’s Honoree for Teaching Excellence
DESCRIPTION:Rainer Doemer awarded Dean’s Honoree for Teaching Excellence by the Office of the Provost and Executive VC to recognize his demonstrated excellence in undergraduate teaching at UCI. Congrats Prof. Doemer!
URL:https://www.cecs.uci.edu/event/rainer-doemer-awarded-deans-honoree-for-teaching-excellence/
LOCATION:CA
CATEGORIES:Awards
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20191031T080000
DTEND;TZID=America/Los_Angeles:20191031T170000
DTSTAMP:20231031T183645Z
CREATED:20231031T183645Z
LAST-MODIFIED:20231031T183645Z
UID:15705-1572508800-1572541200@www.cecs.uci.edu
SUMMARY:Zhou Li and Chaoyi Lu Reached Best paper Award Finalist at ACM IMC’19
DESCRIPTION:Assistant Professor Zhou Li and Chaoyi Lu reached Best paper Award Finalist at the ACM Internet Measurement Conference 2019 with research paper titled\, “An End-to-End\, Large-Scale Measurement of DNS-over-Encryption: How Far Have We Come?” Congrats!
URL:https://www.cecs.uci.edu/event/zhou-li-and-chaoyi-lu-reached-best-paper-award-finalist-at-acm-imc19/
LOCATION:CA
CATEGORIES:Awards
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20191031T080000
DTEND;TZID=America/Los_Angeles:20191031T170000
DTSTAMP:20231031T183836Z
CREATED:20231031T183836Z
LAST-MODIFIED:20231031T183836Z
UID:15709-1572508800-1572541200@www.cecs.uci.edu
SUMMARY:Quoc-Viet Dang Won EECS Professor of the Year
DESCRIPTION:Assistant Professor QV Dang awarded EECS Professor of the Year by UCI Engineering Student Council for his outstanding undergraduate teaching and public services in EECS. Congrats!
URL:https://www.cecs.uci.edu/event/quoc-viet-dang-won-eecs-professor-of-the-year/
LOCATION:CA
CATEGORIES:Awards
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20191031T080000
DTEND;TZID=America/Los_Angeles:20191031T170000
DTSTAMP:20231031T184021Z
CREATED:20231031T184021Z
LAST-MODIFIED:20231031T184021Z
UID:15713-1572508800-1572541200@www.cecs.uci.edu
SUMMARY:Nikil Dutt Won ICS Dean’s Award for Research
DESCRIPTION:Professor Nikil Dutt won ICS Dean’s Award for Research to recognize his innovative research. Congrats Prof. Dutt!
URL:https://www.cecs.uci.edu/event/nikil-dutt-won-ics-deans-award-for-research/
LOCATION:CA
CATEGORIES:Awards
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20191031T080000
DTEND;TZID=America/Los_Angeles:20191031T170000
DTSTAMP:20231031T184139Z
CREATED:20231031T184139Z
LAST-MODIFIED:20231031T184139Z
UID:15717-1572508800-1572541200@www.cecs.uci.edu
SUMMARY:Ardalan Amiri Sani Awarded CAREER Award
DESCRIPTION:Assistant Professor Ardalan Amiri Sani was awarded CAREER Award by the National Science Foundation for the research on “Securing Mobile Devices by Hardening their System Software”. Congrats!
URL:https://www.cecs.uci.edu/event/ardalan-amiri-sani-awarded-career-award/
LOCATION:CA
CATEGORIES:Awards
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20191031T080000
DTEND;TZID=America/Los_Angeles:20191031T170000
DTSTAMP:20231031T184503Z
CREATED:20231031T184435Z
LAST-MODIFIED:20231031T184503Z
UID:15721-1572508800-1572541200@www.cecs.uci.edu
SUMMARY:Marco Levorato Won ICS Dean’s Mid-Career Award for Research
DESCRIPTION:Assistant Professor Marco Levorato won ICS Dean’s Mid-Career Award for Research to recognize his innovative research\, exemplar service\, student mentorship and exceptional teaching skills. Congrats Prof. Levorato!
URL:https://www.cecs.uci.edu/event/marco-levorato-won-ics-deans-mid-career-award-for-research/
LOCATION:CA
CATEGORIES:Awards
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20191031T080000
DTEND;TZID=America/Los_Angeles:20191031T170000
DTSTAMP:20231031T184819Z
CREATED:20231031T184819Z
LAST-MODIFIED:20231031T184819Z
UID:15728-1572508800-1572541200@www.cecs.uci.edu
SUMMARY:Kasra Moazzemi Won 3rd Place at ACM Student Research Competition
DESCRIPTION:Kasra Moazzemi won 3rd Place at ACM Student Research Competition during 2019 ESWEEK for his research titled\, “Learning Coordination for Runtime Resource Management of Heterogeneous Systems”. Congrats Kasra!
URL:https://www.cecs.uci.edu/event/kasra-moazzemi-won-3rd-place-at-acm-student-research-competition/
LOCATION:CA
CATEGORIES:Awards
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20191031T080000
DTEND;TZID=America/Los_Angeles:20191031T170000
DTSTAMP:20231031T185836Z
CREATED:20231031T185836Z
LAST-MODIFIED:20231031T185836Z
UID:15736-1572508800-1572541200@www.cecs.uci.edu
SUMMARY:Yasser Shoukry Won George Corcoran Memorial Award
DESCRIPTION:Assistant Professor Yasser Shoukry won George Corcoran Memorial Award for the exemplary contributions to teaching and educational leadership. Congrats Prof. Shoukry!
URL:https://www.cecs.uci.edu/event/yasser-shoukry-won-george-corcoran-memorial-award/
LOCATION:CA
CATEGORIES:Awards
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20191031T080000
DTEND;TZID=America/Los_Angeles:20191031T170000
DTSTAMP:20231031T190008Z
CREATED:20231031T190008Z
LAST-MODIFIED:20231031T190008Z
UID:15740-1572508800-1572541200@www.cecs.uci.edu
SUMMARY:Daniel Gajski and Lukai Cai Won Test-of-Time Award
DESCRIPTION:Professor Daniel Gajski and Lukai Cai Won Test-of-Time Award at Embedded Systems Week (ESWEEK) 2019 for the paper titled\, “Transaction Level Modeling: An Overview” which defined a new era in embedded computer system design.” Congrats Prof. Gajski and Cai!
URL:https://www.cecs.uci.edu/event/daniel-gajski-and-lukai-cai-won-test-of-time-award/
LOCATION:CA
CATEGORIES:Awards
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: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:20200227T100000
DTEND;TZID=America/Los_Angeles:20200227T110000
DTSTAMP:20231011T190957Z
CREATED:20231011T190957Z
LAST-MODIFIED:20231011T190957Z
UID:14576-1582797600-1582801200@www.cecs.uci.edu
SUMMARY:"Answering Multi-Dimensional Analytical Queries under Local Differential Privacy"
DESCRIPTION:Name: Tianhao Wang\nDate and Time: Thursday\, February 27\, 2020 \nLocation: Engineering Hall 2430 \nAbstract: \nWhen collecting information\, local differential privacy (LDP) relieves users’ privacy concerns\, as it adds noise to users’ private information.  The LDP technique has been deployed by Google\, Apple\, and Microsoft for data collection and monitoring.  In this talk\, I will share the key algorithms we developed in a Chinese e-commercial company Alibaba.  We study the problem of answering multi-dimensional queries under LDP.  Several algorithms are proposed to handle queries with different types of predicates and aggregation functions.  We built a prototype that enables different departments to collect\, share\, and analyze data within the company. \nBiography: \nTianhao Wang is a Ph.D. candidate in the department of computer science\, Purdue University\, advised by Prof. Ninghui Li. He received his B.Eng. degree from software school\, Fudan University in 2015. His research interests include differential privacy and local differential privacy. He is a recipient of the Bilsland Dissertation Fellowship and the Emil Stefanov Memorial Fellowship. He was a member of DPSyn\, which won the second-place award in the NIST PSCR Differential Privacy Synthetic Data Challenge.
URL:https://www.cecs.uci.edu/event/answering-multi-dimensional-analytical-queries-under-local-differential-privacy/
LOCATION:CA
CATEGORIES:Seminars at CECS
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: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:20201031T080000
DTEND;TZID=America/Los_Angeles:20201031T170000
DTSTAMP:20231031T190314Z
CREATED:20231031T190314Z
LAST-MODIFIED:20231031T190314Z
UID:15744-1604131200-1604163600@www.cecs.uci.edu
SUMMARY:Yeseong Kim and Mohsen Imani Reached Best Paper Finalist at IEEE/ACM DATE 2020
DESCRIPTION:Researcher Yeseong Kim and Assistant Professor Mohsen Imani Reached Best Paper Finalist at IEEE/ACM Design\, Automation\, and Test in Europe Conference 2020 for research paper “GenieHD: Efficient DNA Pattern Matching Accelerator Using Hyperdimensional Computing.” Congrats Kim and Prof. Imani!
URL:https://www.cecs.uci.edu/event/yeseong-kim-and-mohsen-imani-reached-best-paper-finalist-at-ieee-acm-date-2020/
LOCATION:CA
CATEGORIES:Awards
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20201031T080000
DTEND;TZID=America/Los_Angeles:20201031T170000
DTSTAMP:20231031T190530Z
CREATED:20231031T190530Z
LAST-MODIFIED:20231031T190530Z
UID:15748-1604131200-1604163600@www.cecs.uci.edu
SUMMARY:Zhou Li and Chaoyi Lu Won IRTF ANRP Award
DESCRIPTION:Assistant Professor Zhou Li and Chaoyi Lu won the Internet Research Task Force (IRTF) Applied Networking Research Prize (ANRP) Award for research paper titled\, “An End-to-End\, Large-Scale Measurement of DNS-over-Encryption: How Far Have We Come?” Congrats Prof. Li and Lu!
URL:https://www.cecs.uci.edu/event/zhou-li-and-chaoyi-lu-won-irtf-anrp-award/
LOCATION:CA
CATEGORIES:Awards
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20201031T080000
DTEND;TZID=America/Los_Angeles:20201031T170000
DTSTAMP:20231031T190812Z
CREATED:20231031T190812Z
LAST-MODIFIED:20231031T190812Z
UID:15752-1604131200-1604163600@www.cecs.uci.edu
SUMMARY:Chuangtao Chen and Mohsen Imani Reached Best Paper Finalist at IEEE/ACM ICCAD 2020
DESCRIPTION:Assistant Professor Mohsen Imani and Chuangtao Chen reached Best Paper Finalist at IEEE/ACM International Conference on Computer-Aided Design 2020 for research paper titled\, “Optimally Approximated and Unbaised Floating-Point Multiplier with Runtime Configurability.” Congrats Chen and Imani!
URL:https://www.cecs.uci.edu/event/chuangtao-chen-and-mohsen-imani-reached-best-paper-finalist-at-ieee-acm-iccad-2020/
LOCATION:CA
CATEGORIES:Awards
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20201031T080000
DTEND;TZID=America/Los_Angeles:20201031T170000
DTSTAMP:20231031T191206Z
CREATED:20231031T191206Z
LAST-MODIFIED:20231031T191206Z
UID:15756-1604131200-1604163600@www.cecs.uci.edu
SUMMARY:Hamid Nejatollahi Reached Best Paper Award Finalist ICASSP 2020
DESCRIPTION:Hamid Nejatollahi reached Best Paper Award Finalist at the International Conference on Acoustics\, Speech and Signal Processing 2020 for research paper titled\, “Exploring Energy Efficient Quantum-resistant Signal Processing Using Array Processors”. Congrats Nejatollahi!
URL:https://www.cecs.uci.edu/event/hamid-nejatollahi-reached-best-paper-award-finalist-icassp-2020/
LOCATION:CA
CATEGORIES:Awards
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