Archive: October 2021

December 1st, 2021

Yi-Chung Chen

Associate Professor

Tennessee State University

Email:

ychen@tnstate.edu

Personal webpage

https://yichungchen84.github.io/

Research interests

Application-specific system, 3-D integration, Heat simulation, NVM, Data pipeline, Deep learning

Short bio

Yi Chung Chen is currently an Assistant Professor in the Department of Electrical and Computer Engineering, Tennessee State University. He received the Ph.D. degree from Electrical and Computer Engineering, University of Pittsburgh, USA in 2014, and the M.S. degree in Electrical and Computer Engineering from New York University, New York in 2011. He has served as TPC member in many conference committees. He has served as committee member of regional and national STEM education committees for silicon and digital system education of underrepresented minorities. He has also served as organizing and technical program committee members of conferences.

Reasearch highlights

Prof. Chen’s has published interdisciplinary papers in the major research fields across computing systems and applications. His research contribution lies in vertical integration of EDA tools for design of application-specific computer system. He investigates data-driven intelligent systems for adaptive, resilient, and expandable operations demanded by critical missions. His research projects are supported by the US AF, the US ARMY, ASEE, and NSF. In addition, Prof. Chen is an acting committee member of semiconductor research and education working group for minority serving institutions. He is on a mission to prepare future semiconductor workforce of underrepresented groups in engineering by building education and research capacity with opensource EDA tools.

December 1st, 2021

Hussam Amrouch

Jun.-Professor

University of Stuttgart, Germany

Email:

amrouch@iti.uni-stuttgart.de

Personal webpage

https://www.iti.uni-stuttgart.de/en/institute/team/Amrouch/

Research interests

Beyond-CMOS, Beyond von-Neumann Architectures, Neuromorphic Computing, Semiconductor Physics, Machine Learing for CAD

Short bio

Hussam Amrouch is a Jun.-Professor heading the Chair of Semiconductor Test and Reliability (STAR) in the Computer Science, Electrical Engineering Faculty at the University of Stuttgart, Germany as well as he is a Research Group Leader at the Karlsruhe Institute of Technology (KIT), Germany. He earned in 06.2015 his Ph.D. degree in Computer Science (Dr.-Ing.) from KIT with the highest distinction (summa cum laude), which has an acceptance ratio of less than 10% at KIT. After which he had founded the “Dependable Hardware” research group at KIT, which he is still leading until now. In 07.2020, He was appointed at the University of Stuttgart, computer science department, as a Junior Professor leading the research efforts in the area of machine learning for CAD with a special focus on design for testing and reliability for advanced and emerging nanotechnologies.

Reasearch highlights

Prof. Amrouch has published more than 140 multidisciplinary publications (including 55 journals) in the major research areas across the computing stack (semiconductor physics, circuit design, computer-aided design, and computer architecture). His key research interests are focused on beyond-CMOS technologies, emerging memories, and beyond-von Neumann architectures with a special focus on In-Memory Computing, Neuromorphic Computing and AI applications. He received eight times a HiPEAC Paper Award. He also received three Best Paper Award Nominations for his work in reliability; two of them from the Design Automation Conference (DAC’17, DAC’16) and one from the Design, Automation and Test in Europe Conference (DATE’17). He has 10 tutorials and 24 invited talks (including 2 keynotes) in several top international conferences (e.g., DAC, DATE, etc.), universities and companies. He has also organized 9 special sessions in top CAD conferences. He currently serves as Review Editor at the Frontiers in Electronics and Associate Editor Integration, the VLSI Journal. He serves also as Technical Program Committee (TPC) member for many top international conferences in the computer science area like Design Automation Conference (DAC). He is also a reviewer in many top journals in different research fields starting from the system level (e.g., IEEE Transactions on Computers TC) to the circuit level (e.g., IEEE Transactions on Circuits and Systems TCAS-I) all the way down to semiconductor physics (e.g., IEEE Transactions on Electron Devices TED).

December 1st, 2021

Bei Yu

Associate Professor

Chinese University of Hong Kong

Email:

byu@cse.cuhk.edu.hk

Personal webpage

http://www.cse.cuhk.edu.hk/~byu/index.html

Research interests

Physical Design, Mask Optimization, Design Space Exploration, Deep Learning

Short bio

Prof. Bei Yu is currently an Associate Professor in the Department of Computer Science and Engineering, The Chinese University of Hong Kong. He received the Ph.D degree from Electrical and Computer Engineering, University of Texas at Austin, USA in 2014, and the M.S. degree in Computer Science from Tsinghua University, China in 2010. He has served as TPC Chair of ACM/IEEE Workshop on Machine Learning for CAD, and in many journal editorial boards and conference committees. He is Editor of the IEEE TCCPS Newsletter.

Prof. Yu has published more than 200 research papers, mainly in top journals (including 39 IEEE TCAD) and top conferences (including 22 DAC and 29 ICCAD) in the VLSI CAD area. He published the most papers of DAC 2019 (7 papers) and ICCAD 2021 (9 papers) among all scholars around the world. He received seven Best Paper Awards from ASPDAC 2021, ICTAI 2019, Integration, the VLSI Journal in 2018, ISPD 2017, SPIE Advanced Lithography Conference 2016, ICCAD 2013, ASPDAC 2012 and five other Best Paper Award Nominations (DATE 2021, ASPDAC 2019, DAC 2014, ASPDAC 2013, and ICCAD 2011). He also received six awards in ICCAD/ISPD contest awards.

Reasearch highlights

As one of the pioneers, Prof. Yu’s research contribution lies in machine learning for EDA, which is to remarkably improve circuit design efficiency with the aid of machine learning techniques. He investigates generative adversarial network models GAN-OPC (DAC’18, TCAD’20) and DAMO (ICCAD’20, TCAD’21) to improve mask optimization quality and efficiency and even outperform state-of-the-art commercial tool. He is pioneer for new class of research about graph learning and point cloud embedding for EDA. For instance, he proposes graph neural network based learning on netlist-level, and investigates how graph learning model can help on testability, reliability, and manufacturability analysis (published on DAC’19, DAC’20, and TCAD’21). He is the first researcher exploiting deep point cloud embedding concept in VLSI physical design field to construct a routing tree (best paper award at ASPDAC’21). In addition, he proposes active learning (equipped with Gaussian process and neural process) as an advanced learning paradigm for design space exploration in EDA (published on TCAD’19 and TCAD’21).

Another distinctive contribution of Prof. Yu’s work is EDA for deep learning system. The resource consumption of the deep learning models is a major concern regarding the broad deployment on resource-constrained hardware. Prof. Yu investigates a unified approximation framework to compress and accelerate the deep learning models, where the low-rankness and structured sparsity are incorporated for model pruning. This work received the best student paper award from ICTAI’2019. He also proposes to optimize the HLS and TVM deployment strategies of DNN models on FPGA and GPU, cooperated with a set of advanced learning and optimization methodologies (published on 2x DATE’2021, 2x ICCAD’21 and ICCV’21). These methodologies facilitate DNN deployment on resource-constrained hardware with high efficiency and performance.