Vickie Winston

Friday, January 26, 2024
10:20 a.m. – 11:10 a.m. (CST)
ETB 1020

Wantong Li
PhD Candidate, School of Electrical and Computer Engineering
Georgia Institute of Technology

Title: “Efficient, Robust, and Heterogeneous Compute-in-Memory for Edge Intelligence”

Abstract 
Deep neural networks (DNNs) have provided remarkable performance gains in fields spanning computer vision and natural language processing. The increasingly heavier workloads to run DNN models have attracted interdisciplinary efforts to speed up DNN inference, and many of such efforts focus on accelerating the dominant multiply-and-accumulate (MAC) operations. On the hardware front, the disruptive paradigm of compute-in-memory (CIM) aims to process data directly inside memory arrays. Complex arithmetic operations such as MAC can be performed using compute-capable CIM arrays to achieve massive parallelism and unprecedented energy efficiency.

This talk focuses on the IC design and hardware architecture aspects of CIM for executing DNN workloads. The talk first presents a mixed-signal CIM engine that performs parallelized MAC operations inside resistive random-access memory (RRAM) arrays. The RRAM-based CIM chip, fabricated in TSMC 40-nm node, is equipped with circuit designs that ensure PVT-robust operations across a wide spectrum of operating conditions. Next, the unique opportunities to transform computing inside a heterogeneous 3-D (H3D) stack are discussed. An H3D CIM accelerator targeting vision transformer models is shown to gain form factor and energy efficiency enhancements. Finally, low-power portable ultrasound imaging is presented to showcase how CIM can benefit embedded electronics. Through data volume reduction of the ultrasound frontend and a local CIM-based reconstruction accelerator, significant power savings can be achieved for the portable imaging device.

Biography
Wantong Li is a Ph.D. candidate in electrical and computer engineering at the Georgia Institute of Technology. He received a BSEE degree from Washington University in St. Louis in 2015 and a MSEE degree from Columbia University in 2016. From 2017 to 2019, he worked as an IC Design Engineer at Power Integrations. He also held internship positions at AMD, MediaTek, and Roche Diagnostics. He is the recipient of the Georgia Tech ECE INSPIRE Fellowship in 2023. His research centers on memory-centric computing platforms, spanning areas of efficient and robust IC design, heterogeneous 3-D integrated systems, and domain-specific architecture.

 

More on CESG Seminars: HERE

Please join on Friday, 1/26/24 at 10:20 a.m. in ETB 1020.

Friday, January 19, 2024
10:20 a.m. – 11:10 a.m. (CST)
ETB 1020

Dr. Kevin Nowka
Professor of Practice, Department of Electrical and Computer Engineering
Texas A&M University

Title: “Systems and Machine Learning Research for Application in Digital Agriculture“

Abstract 
Agricultural practices developed during the Green Revolution of the 1960s to 1980s are insufficient to deal with future global demands for food, especially with increasing natural resource controls. Digital Agriculture research is allowing for continued improvements in crop yields and crop quality with better management practices and more efficient resource utilization.  This talk will cover how use of large agricultural datasets and modern machine learning allows agricultural researchers and producers to improve predictability of crop health and crop yields in support of improved agricultural management practices. Recent research on integration of ML with crop imaging from drones and satellites for wheat, cotton, and sorghum will be presented. Finally, integration of learning systems into agriculture infrastructure will be described.

Biography
Dr. Kevin Nowka is a Professor of Practice in the Texas A&M University Department of Electrical and Computer Engineering. His research focuses on optimizing computer hardware and software and learning models for data-intensive, cognitive and AI applications.

He received a B.S. in Computer Engineering from Iowa State University in 1986 and M.S. and Ph.D. degrees in Electrical Engineering from Stanford University in 1986 and 1995, respectively.

Previously he was the Director of IBM Research – Austin, one of IBM’s 12 global research laboratories and was the IBM Senior State Executive for Texas. Prior to coming to IBM he was a Member of Technical Staff at AT&T Bell Labs.

Dr. Nowka has been granted 135 US Patents and has over 100 publications.

More on Kevin Nowka
https://www.linkedin.com/in/kevin-nowka-6587715b/ or https://www.researchgate.net/profile/Kevin-Nowka

More on CESG Seminars: HERE

Please join on Friday, 1/19/24 at 10:20 a.m. in ETB 1020.

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.

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

Dr. Neena Iman
Director of the O’Donnell Data Science and Research Computing Institute (DSRCI)
Southern Methodist University (SMU)

Title: “Future of Data Science: HPC+AI+Beyond-Moore“

Abstract 

The computing ecosystem is at an inflection point with many disruptive technologies merging together. With the debut of exascale supercomputers recently, the architecture of the next generation of HPC platforms is being discussed in the scientific community. The definition of HPC has changed. HPC is no longer just about floating-point operations, but also about the ability to ingest and process huge amounts of data. The traditional HPC applications/workloads are benefiting from the incorporation of AI and machine learning. Additionally, with the plateauing of Moore’s law, there is tremendous momentum in Beyond-Moore technologies, particularly in quantum. This talk will discuss the future of data science in this rapidly changing technology landscape.

Biography
Dr. Neena Iman is the inaugural Director of the O’Donnell Data Science and Research Computing Institute (DSRCI) at Southern Methodist University (SMU), a position key to the university’s commitment to data-focused education and next-gen computational research. The DSRCI also serves as the gateway to SMU’s HPC environment. Before joining SMU,

Neena Imam served as the Director of Strategic Researcher Engagement at NVIDIA corporation, the industry-leader in GPU computing and AI/ML research. In this role, Neena worked with academic researchers to enable GPU-accelerated and AI/ML applications development. Before NVIDIA, Neena served as a distinguished scientist and the Director of Research Collaboration in the Computin

g and Computational Sciences Directorate at Oak Ridge National Laboratory (ORNL). At ORNL, Neena performed research in HPC, as well as next-generation microelectronics and Post Moore computing.  Neena is the author/co-author of many scientific articles, served as an invited speaker and panelist at many conferences, and is active in professional organizations to promote research and education in HPC and AI.

Neena holds a Doctoral degree in Electrical Engineering from Georgia Institute of Technology, with Master’s and Bachelor’s degrees in the same field from Case Western Reserve University and California Institute of Technology, respectively. Neena also served as the Science and Technology Fellow for Senator Lamar Alexander in Washington D.C. (2010-2012). Neena is a senior member of IEEE,  served as an IEEE officer for multiple years, and is the founding Chair of ACM SIGHPC ASCAN (Accelerated Scalable Computing and ANalytics) chapter.

More on CESG Seminars: HERE

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

Friday, October 27, 2023
10:20 a.m. – 11:10 a.m. (CST)
ETB 1020

Doug Burger
Technical Fellow | Microsoft

Title: “The New AI Computing Stack“

Talking Points:

• Emergent capabilities of large language models, built on top of deep learning
• The intersection of traditional computing and the AI stack
• The evolution of the AI stack, key challenges and problems
• Future implications of the tandem working of the traditional and AI stacks

Abstract
We have entered a new era of computing.  Large language models, built on top of deep learning, have shown surprising emergent capabilities.

These capabilities, such as rich semantic understanding, ability to generate content, and ability to plan and reason, will change how people use computers and for what computers can effectively be used.

This AI stack is effectively a second general class of computing that intersects with the traditional computing stack in surprising and compelling ways.

In this talk I will discuss how the AI stack is evolving, some of the key challenges that we are currently facing, and the most important problems to be working on in this area.

If we are successful, and these challenges are solved, these two computing stacks working in tandem will transform and up-level humanity’s capabilities.

Biography
Doug Burger is a Technical Fellow at Microsoft.  From 1999-2008, he served on the Computer Sciences faculty at UT-Austin, where he co-led the TRIPS project with Steve Keckler.

From 2008-2018, he was a researcher in Microsoft Research, where he led the Catapult and Brainwave projects, which both shipped at large scale in Microsoft’s datacenter infrastructure.

From 2018-2022 he served as a product executive in Azure’s new hardware group, leading teams architecting large-scale AI infrastructure.

In 2023, he returned to Microsoft Research to help drive advanced thinking in AI-based computing.  He is a Fellow of the ACM and the IEEE.

More on CESG Seminars: HERE

Please join on Friday, 10/27/23 at 10:20 a.m. in ETB 1020.

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.

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.