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DTSTART;TZID=America/Los_Angeles:20181101T150000
DTEND;TZID=America/Los_Angeles:20181101T160000
DTSTAMP:20231011T190843Z
CREATED:20231011T190827Z
LAST-MODIFIED:20231011T190843Z
UID:14545-1541084400-1541088000@www.cecs.uci.edu
SUMMARY:Accelerated Computing for Edge-Centric IoT
DESCRIPTION:Name: Tulika Mitra\nDate and Time: Monday\, November 5 at 2:00 p.m. – 3:00 p.m. \nLocation: Donald Bren Hall 3011 \nAbstract: Internet of Things (IoT)\, an ever-growing network of billions of devices embedded within physical objects\, is revolutionizing our daily life. The IoT devices in the edge are primarily responsible only for collecting and communicating the data to the cloud\, where the computationally intensive data analytics takes place. However\, the data privacy and the connectivity issues—in conjunction with the fast real-time response requirement of certain IoT applications—call for smart edge devices that should be able to support privacy-preserving\, time-sensitive computation for machine intelligence on-site. In this talk\, I will present the computation challenges in edge-centric IoT and introduce hardware-software co-designed approaches to overcome these challenges. I will discuss the  design of configurable\, customizable accelerators that are completely software programmable and can be universally deployed across diverse domains to speed up computation and realize the edge analytics vision at ultra-low power budget. I will also demonstrate the potential to achieve low-power\, real-time intelligence on the IoT edge devices via collaborating computing that engages all the on-chip heterogeneous compute elements (CPU\, GPU\, reconfigurable computing\, and accelerators) in a synergistic fashion though sophisticated compile- and runtime strategies. \nBiography: Tulika Mitra is a Professor of Computer Science at School of Computing\, National University of Singapore (NUS). Her research interests span various aspects of the design automation of embedded real-time systems with particular emphasis on low-power computing\, heterogeneous computing\, application-specific processors\, and software timing analysis/optimizations. She has authored over hundred and fifty scientific publications in leading international journals and conferences and holds multiple US patents. Her research has been recognized by best paper award and nominations in leading conferences. She is the recipient of the Indian Institute of Science Outstanding Woman Researcher Award and is an IEEE Distinguished Visitor.  Prof. Mitra currently serves as Senior Associate Editor of the ACM Transactions on Embedded Computing Systems\, Deputy Editor-in-Chief of IEEE Embedded Systems Letters\, and Associate Editor of IEEE Design & Test Magazine. She has served as Associate Editor of IEEE TCAD and organizing/program committee member of almost all major conferences in embedded systems\, real-time systems\, and electronic design automation including program chair of EMOSFT and CASES.
URL:https://www.cecs.uci.edu/event/accelerated-computing-for-edge-centric-iot/
LOCATION:CA
CATEGORIES:Seminars at CECS
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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
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