Who’s Xiaoming Chen

March 1st, 2022

Xiaoming Chen

Associate Professor

Institute of Computing Technology,
Chinese Academy of Sciences

Email:

chenxiaoming@ict.ac.cn

Personal webpage

http://people.ucas.edu.cn/~chenxm

Research interests

EDA and computer architecture

Short bio

Xiaoming Chen received the B.S. and Ph.D. degrees in Electronic Engineering from Tsinghua University, in 2009 and 2014, respectively. Since 2017, he has been an associate professor at Institute of Computing Technology, Chinese Academy of Sciences (ICT, CAS). Before joining ICT, CAS, he was a postdoctoral research associate in Electrical and Computer Engineering, Carnegie Mellon University from 2014 to 2016, and a visiting assistant professor in Computer Science and Engineering, University of Notre Dame from 2016 to 2017.

His research interests are mainly focused on EDA and computer architecture. He has published about 100 papers in top journals and conference proceedings, including DAC, ICCAD, DATE, HPCA, IEEE TCAD, IEEE TVLSI, IEEE TPDS, etc. He has served as a technical program committee member for DAC, ICCAD, ASP-DAC, GLSVLSI, AsianHOST, VLSI Design, etc. He was awarded the Excellent Young Scientists Fund of National Natural Science Foundation of China in 2021. He received the 2015 EDAA Outstanding Dissertation Award and the 2018 Alibaba DAMO Academy Young Fellow Award. He received one of the two best paper awards in ASP-DAC 2022 and several best paper nominations in ASP-DAC and ISLPED.

Reasearch highlights

Prof. Xiaoming Chen has spent more than 10 years in the EDA trarea. Specifically, he has developed a parallel sparse direct solver named NICSLU that is well suited for SPICE-based circuit simulators. He proposed a series of novel techniques to elevate the performance of solving highly sparse linear systems from circuit simulation applications, including a new matrix ordering method to minimize fill-ins, a hybrid dynamic scheduling method for parallel matrix factorization, a numerically stable pivoting reduction technique, and an adaptive numerical kernel selection method. NICSLU achieves much higher performance than other sparse solvers in circuit simulation applications, and is also generally faster than state-of-the-art GPU-based solvers which are specially designed for circuit matrices. NICSLU has been used in a number of academic studies, EDA tools and power system simulators. Some techniques have been adopted in commercial SPICE tools of a leading EDA company in China. NICSLU is available at https://github.com/chenxm1986/nicslu.

Prof. Chen has also made important contributions in computing-in-memory (CiM) architecture design. He exposed how to utilize the device-level CiM feature of resistive random-access memories (RRAMs) and ferroelectric field-effect transistors (FeFETs) which can act as both storage elements and switch units, to unify the computing and storage resources at the circuit level, to realize interchangeable computing and storage functionalities at the architecture level. In addition, he has investigated the solutions to some fundamental problems in CiM systems, including data coherence, data contention, simulation methodologies, task assignment, etc. He has also explored the CiM feature of RRAMs and FeFETs in various applications, and designed energy-efficient and high-performance accelerators for neural networks, graph processing, linear algebra, and robots.

Who’s Kai Ni

January 1st, 2022

Kai Ni

Assistant Professor

Rochester Institute of Technology

Email:

kai.ni@rit.edu

Personal webpage

https://www.needskai.org/

Research interests

Emerging Devices for AI Accelerator, Emerging Devices for Unconventioanl Computing

Short bio

Kai Ni received the B.S. degree in Electrical Engineering from University of Science and Technology of China, Hefei, China in 2011, and Ph.D. degree of Electrical Engineering from Vanderbilt University, Nashville, TN, USA in 2016 by working on characterization, modeling, and reliability of III-V MOSFETs. Since then, he became a postdoctoral associate at University of Notre Dame, working on ferroelectric devices for nonvolatile memory and novel computing paradigms. He is now an assistant professor in Electrical & Microelectronic Engineering at Rochester Institute of Technology. He has around 100 publications in top journals and conference proceedings, including Nature Electronics, IEDM, VLSI Symposium, IRPS, EDL, etc. He has served as technical program committee for DAC, DATE, ASPDAC, IRPS, EDTM. His current interests lie in nanoelectronic devices empowering unconventional computing, domain-specific accelerator, and memory technology.

Reasearch highlights

Kai Ni has made important contributions to the development of ferroelectric HfO2 based field effect transistor (FeFET) and its technology applications. On the technology side, he has proposed the ferroelectric metal field effect transistor, which has a metal-ferroelectric-metal-oxide-semiconductor gate stack and has the freedom of optimizing the gate stack, and superlattice structure for multi-level cell. He has developed several models for FeFET explaining different behaviors of FeFET, including a compact model based on the Preisach model of ferroelectric, a Kinetic Monte Carlo model to explain device variation, and a comprehensive model which can capture all the key ferroelectric behaviors. With these models, he also explored the exciting applications of FeFET for in-memory computing. Examples include the crossbar array for matrix-vector multiplication, content addressable memory array for associative search, hardware security circuit, and reconfigurable computing. All these research activities have been published in top journals, and premier conferences, such as IEDM, VLSI Symposium, DAC, DATE, etc.

Prof. Rob Rutenbar receives the 2021 ACM SIGDA Pioneering Achievement Award

The SIGDA award selection committee is honored to announce that Prof. Rob Rutenbar has been selected to receive the 2021 ACM SIGDA Pioneering Achievement Award.

for his pioneering work and extraordinary leadership in analog design automation and general EDA education.

As the highest technical distinction of ACM SIGDA, this award is to recognize the lifetime of outstanding achievements on Electronic Design Automation.

This award will be presented in SIGDA Annual Member Meeting and Dinner at ICCAD 2022.