Department of Electrical and Computer Engineering

The Computer Engineering and Systems Group

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CESG Seminar: Azalia Mirhoseini

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Friday, December 1, 2023
10:20 a.m. – 11:10 a.m. (CST)
ETB 1020

Dr. Azalia Mirhoseini
Assistant Professor, Department of Computer Science
Stanford University

Title: “Pushing the Limits of Scaling Laws in the Age of Large Language Models“

Abstract 
The recent success of large language models has been characterized by scaling laws – the power law relationship between performance and training dataset size, model parameter size, and training compute. In this talk, we will discuss ways to push the scaling laws even further by innovating across data, models, software and hardware. This includes reinforcement learning from human and AI feedback to improve learning efficiency, sparse and dynamic mixture-of-experts neural architectures for better performance, an automated framework for co-designing custom AI accelerators, and a deep RL method for chip floorplanning used in multiple generations of Google AI’s accelerator chips (TPU). Through these cutting-edge examples, we will outline a full-stack approach that leverages AI to overcome the next set of scaling challenges.

Biography
Dr. Azalia Mirhoseini is an assistant professor of computer science at Stanford University and a senior staff research scientist at DeepMind. Her research interest is developing capable and efficient AI systems that can solve high-impact, real-world problems. Before joining Stanford, Prof. Mirhoseini spent several years in industry, working on frontier generative AI and deep reinforcement learning projects at Anthropic and Google Brain. She has led a diverse portfolio of AI and Systems projects, with publications in Nature, ICML, ICLR, NeurIPS, UAI, ASPLOS, SIGMETRICS, DAC, DATE, and ICCAD. She has received a number of awards, including the MIT Technology Review 35 Under 35, the Best Ph.D. Thesis at Rice University’s ECE Department, and a Gold Medal in the National Math Olympiad in Iran. Her work has been covered in various media outlets, including MIT Technology Review, IEEE Spectrum, The Verge, Times of London, ZDNet, VentureBeat, and WIRED.

More on Azalia Mirhoseini: http://azaliamirhoseini.com/

More on CESG Seminars: HERE

Please join on Friday, 12/1/23 at 10:20 a.m. in ETB 1020.

NEWS! – Dr. Dileep Kalathil and Dr. Moble Benedict receive Office of Naval Research (ONR) grant

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Dr. Dileep Kalathil and Dr. Moble Benedict received  an Office of Naval Research (ONR) grant to study Autonomous VTOL Aircraft Ship Landing. The team is geared to develop the next generation of fully autonomous vertical takeoff and landing (VTOL) aircraft on ships under rough conditions by combining an optimal aircraft design with a robust reinforcement learning control algorithm so precise that even if a vehicle is changing course or is in the presence of heavy winds, it can still track the horizon bar on the ship, which is a green, lighted, gyro-stabilized strip that provides the pilot an artificial horizon.

Dr. Benedict and Dr. Kalathil have proven success in using reinforcement learning to track and safely land an unmanned aerial system (UAS) in various conditions, including moderate horizontal winds, foggy visibility and changes in course and speed. Now, they’re merging their respective disciplines of aerospace engineering and electrical and computer engineering to build on these advancements.

More on this HERE

       

NEWS! – Dr. Srinivas Shakkottai and Dr. Dileep Kalathil receive National Science Foundation (NSF) grant

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Principal investigator Dr. Srinivas Shakkottai and co-principal investigator Dr. Dileep Kalathil recently received a National Science Foundation (NSF) grant to research EdgeRIC: Real-time radio access network intelligent control for the next generation of cellular networks. In their lab, Shakkottai and Kalathil are conducting experiments to show how EdgeRIC operates and the significance of real-time control happening every millisecond. Joining the project is Dr. Dinesh Bharadia, an associate professor from the University of California San Diego and an adjunct professor at Texas A&M.

More on this at https://engineering.tamu.edu/news/2023/09/engineering-researchers-to-study-wireless-communication-and-machine-learning-with-nsf-grant.html

       

 

 

 

 

 

CESG Seminar: Abu Sebastian

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Friday, October 20, 2023
10:20 a.m. – 11:10 a.m. (CST)
ETB 1020

Abu Sebastian
Distinguished Research Scientist, IBM Research – Europe

Title: “Analog In-Memory Computing for Deep Learning Inference“

Talking Points: 

  • What is analog in-memory computing (AIMC)?
  • How mature is AIMC?
  • What are the key open research topics and what’s next for AIMC?

Abstract
I will introduce analog in-memory computing based on non-volatile memory technology with a focus on the key concepts and the associated terminology. Subsequently, a multi-tile mixed-signal AIMC chip for deep learning inference will be presented. This chip fabricated in 14nm CMOS technology comprises 64 AIMC cores/tiles based on phase-change memory technology. It will serve as the basis to delve deeper into the device, circuits, architectural and algorithmic aspects of AIMC. Of particular focus will be achieving floating point-equivalent classification accuracy while performing the bulk of computations in the analog domain with relatively less precision. I will also present an architectural vision for a next generation AIMC chip for DNN inference. I will conclude with an outlook for the future.

Two papers that may be of interest:

  1. “Y2023_legallo_NatureElectronics.pdf”

  2. “Y2020_sebastian_NatureNano.pdf”

Biography
Abu Sebastian is one of the technical leaders of IBM’s research efforts towards next generation AI Hardware and manages the in-memory computing group at IBM Research – Zurich. He is the author of over 200 publications in peer-reviewed journals/conference proceedings and holds over 90 US patents.  In 2015 he was awarded the European Research Council (ERC) consolidator grant and in 2020, he was awarded an ERC Proof-of-concept grant. He was an IBM Master Inventor and was named Principal and Distinguished Research Staff Member in 2018 and 2020, respectively. In 2019, he received the Ovshinsky Lectureship Award for his contributions to “Phase-change materials for cognitive computing”. In 2023, he was conferred the title of Visiting Professor in Materials by University of Oxford. He is a distinguished lecturer and fellow of the IEEE.

More on Abu Sebastian: https://research.ibm.com/people/abu-sebastian

More on CESG Seminars: HERE

Please join on Friday, 10/20/23 at 10:20 a.m. in ETB 1020.

CESG Seminar: Arif Merchant

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Friday, October 13, 2023
10:20 a.m. – 11:10 a.m. (CST)
ETB 1020

Arif Merchant
Research Scientist @ Google

Title: “Research Directions in Google Storage“

Talking Points: 

  • How does Google Storage work?
  • How do you lay out data across tens of thousands of disks, dozens of device types, with wildly varying failure rates and capacities, while keeping them all well utilized, and the data safe?
  • What are the open research questions in Cloud Storage?

Abstract
Google’s Cloud is a very large consumer of storage, and these storage systems are geographically distributed, multi-layered, heterogeneous, and complex. We present a high-level overview of Google’s storage systems, the common challenges faced by storage at Google, and explore several research directions for managing and optimizing the resources used. We will touch upon topics in layout, encoding, and new storage technologies. We will conclude with a discussion of some open questions.

Biography
Arif Merchant is a Research Scientist at Google and leads the Storage Analytics group, which studies interactions between components of the storage stack. His interests include distributed storage systems, storage management, and stochastic modeling. He holds the B.Tech. degree from IIT Bombay and the Ph.D. in Computer Science from Stanford University. He is an ACM Distinguished Scientist.


More on CESG Seminars: HERE

Please join on Friday, 10/13/23 at 10:20 a.m. in ETB 1020.

Best Student Paper Award

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Congratulations to Dr. Jiang Hu, Ph.D. student Yishuang Lin and former Ph.D. student Yaguang Li!

Their paper “MMM: Machine Learning-Based Macro-Modeling for Linear Analog ICs and ADC/DACs” won the Best Student Paper Award at the 5th ACM/IEEE Workshop on Machine Learning for CAD (MLCAD 2023).

This work introduces macro-model level machine learning techniques to address the problems of huge model construction cost and low model reusability for linear analog ICs and ADC/DACs.

Kudos!

CESG Seminar – Bingzhe Li

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Friday, October 6, 2023
10:20 a.m. – 11:10 a.m. (CST)
ETB 1020

Bingzhe Li
Assistant Professor I Dept. of Computer Science
University of Texas at Dallas

Title: “Next-Generation Storage Systems for Big Data”

Talking Points: 

  • How to efficiently manage current emerging storage devices
  • How to design a new DNA storage system
  • How to build a high-performance storage system for big-data applications

Abstract
Tremendous technology developments have been witnessed in the area of computing, network and storage systems. A huge amount of digital data was generated in the past decades with the rapid growth of new technology development such Internet of Things (IoT) devices, edge devices, sensors, 5G, and so forth. Such vast amounts of digital data are being generated and are available for access to all new applications. It becomes a critical and increasing challenge to manage this huge amount of data available at our fingertips and to locate the information that we need at anytime from anywhere.

In this talk, I will focus on emerging storage systems for big data from two perspectives. First, from the capacity perspective, two emerging storage devices/systems with large areal densities (i.e., shingled magnetic recording (SMR) and DNA storage) are introduced including their management and utilization. For SMR, based on its unique properties, we introduce a Machine Learning (ML) based scheme to improve the performance of the SMR storage system. Moreover, for image-based applications, we will introduce how to efficiently store images into DNA storage with higher reliability and capacity. Secondly, from the performance perspective, I will present a high-performance system to accelerate graph neural networks (GNNs). The system is co-designed with the storage systems and algorithms in GNNs and finally can significantly speed up the training process of GNNs.

Biography
Dr. Bingzhe Li is currently an assistant professor of Computer Science at the University of Texas at Dallas. He received his PhD degree in Electrical and Computer Engineering from the University of Minnesota, Twin Cities in 2018 after which he worked as a postdoctoral associate in the Department of Computer Science and Engineering, University of Minnesota, Twin Cities.

His research interests focus on memory and storage systems, computer architecture, and low-cost computing architecture. He has served on conference organization committees as well as technical program committees and as reviewer for several major conferences and journals in computer system, storage, and computing architecture. In recognition of his research, he received the Best Paper Nomination at the ICCD’21 and the featured paper of the Month at IEEE Transactions of Computers on March 2021.


More on Bingzhe Li: Bingzhe Li (tamu.edu)

More on CESG Seminars: HERE

Please join on Friday, 10/6/23 at 10:20 a.m. in ETB 1020.

2022 Awards: Dr. Reddy & Dr. Khatri

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Congratulations Dr. Khatri on your 2022 Engineering Faculty Award! Well done!

Congratulations Dr. Reddy on your Engineering Honoree Award!

Congratulations Dr. Hu!

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CESG’s Jiang Hu has a new publication: Machine Learning Applications in Electronic Design Automation by himself and Dr. Haoxing Ren.

This book covers a wide range of the latest research on ML applications in electronic design automation (EDA), including analysis and optimization of digital design, analysis and optimization of analog design, as well as functional verification, FPGA and system level designs, design for manufacturing, and design space exploration. The ML techniques covered in this book include classical ML, deep learning models such as convolutional neural networks, graph neural networks, generative adversarial networks and optimization methods such as reinforcement learning and Bayesian optimization.

More information at https://www.barnesandnoble.com/w/machine-learning-applications-in-electronic-design-automation-haoxing-ren/1141727406?ean=9783031130748

Dr. P.R. Kumar – IEEE Alexander Graham Bell Medal

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Dr. Kumar is the 2022 recipient of one of the Institute of Electrical and Electronics Engineers’ (IEEE) most prestigious honors — the IEEE Alexander Graham Bell Medal. It is the highest award by IEEE in communications and networking. Kumar was recognized for his seminal contributions to the modeling, analysis and design of wireless networks.

For more, go to https://engineering.tamu.edu/news/2021/12/kumar-awarded-institute-of-electrical-and-electronics-engineers-medal.html.

Congratulations Dr. Kumar!

Congratulations Dr. Karan Watson!

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Dr. Karan Watson, Regents Professor, was awarded the 2021 American Society for Engineering Education (ASEE) Lifetime Achievement Award in Engineering Education. Dr. Watson was recognized for her pioneering leadership and sustained contributions to education in the fields of engineering and engineering technology.

For the full article or a more in-depth look at her work, please visit: Texas A&M Engineering News and Dr. Watson’s Google Scholar Profile

Past Recipients
2012 Richard M. Felder
2014 James E. Stice
2015 Karl A. Smith
2016 Russ Pimmel
2018 James L. Melsa
2019 K.L. DeVries
2020 Don P. Giddens
2021  Karan L. Watson

CESG Former Student Shiyan Hu Elected to European Academy of Sciences and Arts

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CESG former student, Shiyan Hu, who received his Ph.D. in Computer Engineering in 2008, has been elected as a Member for  European Academy of Sciences and Arts for his significant contributions to Design, Optimization, and Security of Cyber-Physical Systems. European Academy of Sciences and Arts currently has about 2,000 members, including 34 Nobel Prize Laureates, who are world leading scientists, artists, and practitioners of governance, with expertise ranging from Natural Sciences, Medicine, Technical & Environmental Sciences, Humanities, to Social Sciences. Academy members, who are dedicated to innovative research, international collaboration as well as the exchange and dissemination of knowledge, are elected based on their outstanding achievements.

Shiyan Hu is a professor and the Chair in Cyber-Physical System Security and Director of Cyber Security Academy at University of Southampton. He has published more than 150 refereed papers in the area of Cyber-Physical Systems, Cyber-Physical System Security, and VLSI Computer Aided Design, where most of his journal articles appeared in IEEE/ACM Transactions. He is an ACM Distinguished Speaker, an IEEE Systems Council Distinguished Lecturer, a recipient of the 2017 IEEE Computer Society TCSC Middle Career Researcher Award, and a recipient of the 2014 U.S. National Science Foundation CAREER Award. His publications have received distinctions such as the 2018 IEEE Systems Journal Best Paper Award, the 2017 Keynote Paper in IEEE Transactions on Computer-Aided Design, the Front Cover Paper in IEEE Transactions on Nanobioscience in March 2014, multiple Thomson Reuters ESI Highly Cited Papers/Hot Papers, etc. His ultra-fast slew buffering technique has been widely deployed in the industry for designing over 50 microprocessor and ASIC chips such as IBM flagship chips POWER 7 and 8.

He is a well-recognized international leader in his field. He is chairing the IEEE Technical Committee on Cyber-Physical Systems, leading IET Cyber-Physical Systems: Theory & Applications, and chaired the 2020 Editor-in-Chief Search Committee Chair for ACM TODAES. He has served as an Associate Editor

r for 5 IEEE/ACM Transactions such as IEEE TCAD, IEEE TII and ACM TCPS and as a Guest Editor for various IEEE/ACM journals such as Proceedings of the IEEE and IEEE Transactions on Computers. He is an Elected Member of the European Academy of Sciences and Arts, a Fellow of IET, and a Fellow of British Computer Society.

Shiyan Hu says: “I am delighted to be elected as a Member of European Academy of Sciences and Arts. It is a unique honor in recognition of my research accomplishments and international leadership in my research fields. After many years following my graduation, I still feel very grateful to the education I received from Texas A&M’s Computer Engineering Group and research experience with my Ph.D. advisor Professor Jiang Hu. These were pivotally helpful for me to contribute significantly to my fields.”

Agricultural Blue Legacy Award

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Congratulations to Dr. Jiang Hu and team for receiving the Agricultural Blue Legacy Award this March.

They developed a center pivot automation and control system known as CPACS. This contributes to water conservation in the field of agriculture. To learn more, go to http://www.hpwd.org/newswire/2021/3/18/amarillo-water-management-team-honored.

The team is referred to as the “Amarillo Water Management Team” and includes:
Dr. Hongxin Kong, CEEN, PhD Graduate
Jianfeng Song, CEEN, PhD Candidate
Dr. Justin Sun, CEEN, PhD Graduate
Dr. Yanxiang Yang, CEEN, PhD Graduate
Dr. Jiang Hu, co-director of graduate programs in the Texas A&M Department of Electrical and omputer Engineering at College Station;
Dr. Gary Marek, U.S. Department of Agriculture-Agricultural Research Service agricultural engineer at Bushland;
Thomas Marek, AgriLife Research senior research engineer at Amarillo;
Dr. Dana Porter, Texas A&M AgriLife Extension Service program leader in the Department of Biological and Agricultural Engineering at Lubbock; and
Dr. Qingwu Xue, AgriLife Research crop stress physiologist at Amarillo.

Thank you Amarillo Water Management Team for improving our world with your projects!

 

Pic 1: Dr. Hongxin Kong
Pic 2: Dr. Jiang Hu & Dr. Yanxiang Yang
Pic 3: Dr. Hongxin Kong
Feature Pic: Yanxiang Yang, Thomas Marek & Justin Sun

NEWS! – Dr. Dileep Kalathil and Dr. Moble Benedict receive Office of Naval Research (ONR) grant

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Dr. Dileep Kalathil and Dr. Moble Benedict received  an Office of Naval Research (ONR) grant to study Autonomous VTOL Aircraft Ship Landing. The team is geared to develop the next generation of fully autonomous vertical takeoff and landing (VTOL) aircraft on ships under rough conditions by combining an optimal aircraft design with a robust reinforcement learning control algorithm so precise that even if a vehicle is changing course or is in the presence of heavy winds, it can still track the horizon bar on the ship, which is a green, lighted, gyro-stabilized strip that provides the pilot an artificial horizon.

Dr. Benedict and Dr. Kalathil have proven success in using reinforcement learning to track and safely land an unmanned aerial system (UAS) in various conditions, including moderate horizontal winds, foggy visibility and changes in course and speed. Now, they’re merging their respective disciplines of aerospace engineering and electrical and computer engineering to build on these advancements.

More on this HERE

       

CESG Seminar: Dr. JV Rajendran

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Friday, November 17, 2023
10:20 a.m. – 11:10 a.m. (CST)
ETB 1020

Dr. JV Rajendran
Associate Professor | Department of Electrical and Computer Engineering | Texas A&M University

Title: “Hardware Fuzzing — Why? What? How?“

Talking Points:

  • Do you trust your Verilog code?
  • Do you want to learn how to hijack a chip?
  • What keeps CHIP designers up at night?

Abstract
Hardware is at the heart of computing systems. For decades, software was considered error-prone and vulnerable. However, recent years have seen increased attacks exploiting hardware vulnerabilities and exploits, which even traditional software-based protections cannot prevent. In this talk, I will describe what hardware vulnerabilities look like in hardware “programming languages,” such as Verilog and VHDL. Then, I will explain a new and radical approach called hardware fuzzing for finding these vulnerabilities. Finally, I will detail how these new fuzzing techniques can be efficiently combined with existing functional verific

ation and validation approaches.

Biography
Dr. Jeyavijayan (JV) Rajendran is an Associate Professor and an ASCEND Fellow in the Department of Electrical and Computer Engineering at Texas A&M University. He obtained his Ph.D. degree from New York University in August 2015. His research interests include hardware security and computer security. His research has won the NSF CAREER Award in 2017, ONR Young Investigator Award in 2022, the IEEE CEDA Ernest Kuh Early Career Award in 2021, the ACM SIGDA Outstanding Young Faculty Award in 2019, the Intel Academic Leadership Award, the ACM SIGDA Outstanding Ph.D. Dissertation Award in 2017, and the Alexander Hessel Award for the Best Ph.D. Dissertation in the Electrical and Computer Engineering Department at NYU in 2016 along with several best student paper awards. He organizes and has co‐founded Hack@DAC, a student security competition co-located with DAC, and SUSHI.

More Information on Dr. JV Rajendran: HERE

More on CESG Seminars: HERE

Please join on Friday, 11/17/23 at 10:20 a.m. in ETB 1020.

CESG Seminar: Neal Cardwell

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Friday, November 3, 2023
10:20 a.m. – 11:10 a.m. (CST)
ETB 1020

Neal Cardwell
Principal Software Engineer | Google

Title: “Congestion Control in the Real World“

Talking Points:

  • Swift is a delay-based congestion control for datacenters that achieves low latency, high utilization, and near-zero loss.
  • Swift achieves roughly 50 microsecond tail latency while maintaining near 100% utilization even at 100Gbps line rates.
  • BBR is a model-based congestion control for wide-area networks that achieves low latency, high utilization, and robustness to a targeted level of random packet loss.
  • BBR avoids bufferbloat (maintaining bounded queues in buffers of any depth), while maintaining near 100% utilization even at the moderate loss rates characteristic of today’s high-speed, shallow-buffered WANs with bursty short flows.

Abstract
Designing and deploying new high-performance congestion control algorithms at scale in today’s high-speed, real-world datacenter and wide-area networks is challenging. This talk will discuss the challenges in these environments, and then focus on the high-performance congestion control algorithms we have created and deployed at global scale at Google: Swift for datacenter congestion control, and BBR for wide-area congestion control. The talk will close with thoughts on interesting research questions and potential future research directions for real-world congestion control.

Biography
Neal Cardwell is a Principal Software Engineer in Google’s NYC office. He entered the UC Berkeley PhD program in 1996 and then followed his advisor, Tom Anderson, to the University of Washington, where he completed an MS in 1999, with research in the area of TCP congestion control. He worked at Steve McCanne’s FastForward Networks from 1999 to 2002. He has worked at Google since 2002, on projects including GFE (the Google Front End proxying all traffic for google.com), Googlebot (Google’s web crawler), routing performance, the open source Packetdrill network stack testing tool, and Linux TCP congestion control and loss recovery. He is currently a member of the Congestion Control team at Google, and his recent focus has been on improving the BBR and Swift congestion control algorithms.

More Information on Neal Cardwell: https://research.google/people/NealCardwell/

More on CESG Seminars: HERE

Please join on Friday, 11/03/23 at 10:20 a.m. in ETB 1020.

NEWS! – Dr. Srinivas Shakkottai and Dr. Dileep Kalathil receive National Science Foundation (NSF) grant

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Principal investigator Dr. Srinivas Shakkottai and co-principal investigator Dr. Dileep Kalathil recently received a National Science Foundation (NSF) grant to research EdgeRIC: Real-time radio access network intelligent control for the next generation of cellular networks. In their lab, Shakkottai and Kalathil are conducting experiments to show how EdgeRIC operates and the significance of real-time control happening every millisecond. Joining the project is Dr. Dinesh Bharadia, an associate professor from the University of California San Diego and an adjunct professor at Texas A&M.

More on this at https://engineering.tamu.edu/news/2023/09/engineering-researchers-to-study-wireless-communication-and-machine-learning-with-nsf-grant.html

       

 

 

 

 

 

Congratulations Summer 2023 Masters’ and Doctorate Graduates!

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CESG is proud to announce our Summer 2023 graduating students. We celebrate these students who were dedicated to working hard and meeting the demands of faculty, daily life and family concerns during the uncertainties of a world-wide pandemic.  They still came to events and classes in masks or attended online to get done what had to be done to for their education and their degrees.  Perhaps they are therefore better equipped to face the unknown and well-practiced in flexibility and creativity whatever challenges they are faced with in their futures.  CESG and the ECE wishes them success personally and professionally as they enter (or re-enter) their careers in industry or academia full-heartedly.  Congratulations!

Doctorate Degrees

Dr. Archana Bura (Advisor: Dr. Shakkottai)

Dr. Sung Min Hong (Advisor: Dr. Sprintson)

Dr. Jaewon Kim (Advisor: Dr. Kumar)

Dr. Desik Rengarajan (Advisor: Dr. Shakkottai)

 

Masters’ Degrees

Christopher Baldwin

Kushagra Gupta (Thesis Advisor: Dr. Khatri)

Mohamed Jawaharullah

Veronika Maragulova

Ratnadeep Pal

May you each have success and happiness in your futures and continue to strive and live by the Aggie Core Values!

The Commencement Ceremony is held on Saturday, August 12, 2023 at 2:00 p.m. in Reed Arena. (more info.)