Lecture Series Archive

THz Interconnect, the Last Centimeter Communication

Dr. Jane Gu

Dr. Jane Gu
Professor
ECE Department, UC Davis, Davis, CA

Thu, 03/18/2021

Abstract – "Interconnect Gap" has been a long-standing big challenge caused by the gap between the ever-increasing data rate demand of inter-/intra- chip communications and the insufficient capabilities. Existing electrical interconnect (EI) and optical interconnect (OI) face grand barriers to completely address the interconnect issues individually. THz Interconnect (TI), utilizing the frequency spectrum sandwiched between microwave and optical frequencies, holds the high potentials to complement EI and OI by leveraging the advantages of both electronics and optics. In this talk, I will present our research activities in the high potential TI field, including THz silicon waveguide channel development, TI system demonstration, dispersion constrained link bandwidth and mitigation schemes such as FDM and orthomode channelization.

Dr. Qun Jane Gu has received the Ph.D. from University of California, Los Angeles in 2007. Since August 2012, she has been with University of California, Davis, where she is currently a professor. Dr. Jane Gu's group is passionate in high performance RF, mm-wave and THz integrated circuits and systems and its broad applications. The work from her group has won several best paper awards from international conferences. She has received NSF CAREER award, 2015 COE Outstanding Junior Faculty Award, 2017 and 2018 Qualcomm Faculty Award, and 2019 Chancellor Fellow.

Reflecting What AI Perceives from Human Interaction

Dr. Nina Marhamati

Dr. Nina Marhamati
CS Department, Sonoma State University, Rohnert Park, CA

Thu, 03/04/2021

Abstract – Inspired by interactive art pieces and all the excitement about AI surrounding us, we wonder what machines and AI can reflect about humans and the interactions with humans. In this talk, you will hear about an AI system that we are creating to reflect on user interaction and to demonstrate how the way the user approaches the system (machine) is perceived by AI. Different machine learning and state of the art AI methods are used to detect the user's emotional state while interacting with the system. The sensory inputs are processed, and will be fused when possible, to extract information from the interaction and improve the results. The perceived interaction results are demonstrated via animations or other visuals in two or three-dimensional settings. The final product has the potential to be integrated into virtual reality or augmented reality applications. This is an interdisciplinary project with collaborators from different backgrounds in CS, EE, and art.

Dr. Nina Marhamati is an Assistant Professor of Computer Science at Sonoma State University and, on the side, provides machine learning consulting services to the industry. She was an Artificial Intelligence Fellow at Insight Data Science and received her PhD in Computer Science from Southern Illinois University, where she was a DRA Fellow and the Outstanding Graduate Assistant in 2016. She was the recipient of the Best Dissertation Award from North American Fuzzy Information Processing Society in 2017. She is passionate for applications of artificial intelligence and machine learning that make day-to-day life easier and more fulfilling. She enjoys working on interdisciplinary projects and her areas of interest include natural language processing, machine learning & data analysis, Bayesian estimation theory, computing with words, and fuzzy logic.

FPGA Acceleration on Artificial Intelligence

Dr. Xiaokun Yang

Dr. Xiaokun Yang
University of Houston-Clear Lake, Houston, TX

Thu, 02/18/2021

Abstract – Hardware acceleration on Artificial Intelligence (AI) has become an indispensable technique for a wide range of real-time applications such as video classification, speech recognition, and autonomous robot. Specifically in the era of edge computing, to limit the complexity of AI algorithms in the power-constrained and latency-critical scenarios is a big challenge. Therefore, this presentation focuses on mapping neural networks onto field-programmable gate array (FPGA) with the benefits of high parallelism and programmability. Generally, two research subjects are discussed: the design and evaluation to FPGA acceleration and system-on-chip (SoC) architecture to FPGA. First, case studies to the design on FPGA accelerator are presented, including handwritten digit recognition and real-time music transcription. Second, a low-cost and energy-efficient SoC architecture is discussed to the applications of edge devices. By integrating DMA, AES core, bus wrappers, and open verification components (OVCs), a UVM-based environment is finally established to verify the functionality of the SoC.

Dr. Xiaokun Yang received his Ph.D. from the Department of Electrical and Computer Engineering (ECE), Florida International University (FIU), USA in Spring 2016. He is currently an Assistant Professor at the College of Science and Engineering, University of Houston-Clear Lake. From 2007 to 2012, he has also worked as a Senior ASIC Design and Verification Engineer at Advanced Micro Devices (AMD) and China Electronic Corporation (CEC). His research interests include Hardware Acceleration on AI/ML, ASIC/FPGA Design and Verification on Neural Networks, and Energy-Efficiency System-on-Chip (SoC) Architecture. As the first author or corresponding author, Dr. Yang has published more than 50 papers including 3 patents, more than 15 peer-review journals, and more than 30 prestigious international conferences. He has served on several editorial boards and journal reviewers including IEEE Trans. on Computer, IEEE Trans. on VLSI, and IEEE Trans. on Education, and numerous conference committees including ISVLSI and ISQED.

Security of Networked Control Systems

Dr. Arman Sargolzaei

Dr. Arman Sargolzaei
Assistant Professor
ME Department, Tennessee Technological University, Cookeville, TE

Thu, 02/04/2021

Abstract – With the immense growth of networked control systems (NCSs) development and utilization in critical infrastructures such as unmanned aerial vehicles and autonomous systems, assurance of their safety, security, and resiliency are yet a significant challenge for industries. Although defense mechanisms for NCSs have been significantly improved, incorporating smart detection and control platforms, yet a similar growth in the generations and models of cyber-attacks cannot be discarded. Existing control and communication protocol strategies are not fully capable of preventing and responding to new types of cyber-attacks. This requires vulnerability identifications along with smart, collaborative integration of controllers, sensors, actuators, and communication protocols in real-time. The seminar discusses recently introduced a mathematical approach to the Time Delay Switch (TDS) attack as a comprehensive outlook for the new generation of cyber-attacks.

Dr. Arman Sargolzaei's expertise is in applying linear and nonlinear control methods, machine learning, and artificial intelligence to the field of Cyber-Physical Systems. His mission is to enhance the quality of life for people, by assuring the safety, security, and privacy concerns through extensive collaboration among multi-disciplinary fields. His research on the security of Networked Control Systems (NCSs) and resiliency of Multi-agent systems, particularly his doctoral dissertations combined with the knowledge of control theory, system identification, mathematics, and statistics carried significant practical implications in better understanding the pathways of faults, failure, and attack detection and compensation for NCSs. He is recognized with the honor of the "Faculty Research Excellence Award" for two consecutive years. He is currently an Assistant Professor of Mechanical Engineering at Tennessee Technological University. Before joining Tennessee Tech, he was director of Advanced Mobility Institute (AMI) and an Assistant Professor of Electrical Engineering at Florida Polytechnic University.

Probabilistic and Approximate Computation in Software and Hardware Models

Bala Ravikumar

Professor Bala Ravikumar
Professor, Chair
Department of Computer Science, SSU, Rohnert Park, CA

Thu, 11/19/2020

Abstract - Everyone knows that modern digital computers are based on deterministic Boolean logic gates that are assumed to perform exact computation of logical operations such as AND, NOT etc. In this talk, we will explore two alternatives to this framework in which the demand on the output being correct on all inputs is relaxed. First, we will consider a state machine model as a way to approximate the solutions of some computational problems. The second one is a stochastic model of logic gates that provides an alternative hardware basis for building digital computers. Some applications of these models will also be presented.

Dr. Ravikumar received his Ph.D. in Computer Science from the University of Minnesota and has taught at many universities including University of Minnesota, University of Rhode Island, San Francisco State University and Sonoma State University. He has supervised many graduate and undergraduate projects. He is an editor of the International Journal of the Foundations of Computer Science and has served on the program committees of many international conferences. He has presented invited and contributed talks in more than fifty international conferences.

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