Speakers
Luca Benini
Toward AI-Boosted Software-Defined RANs on open RISC-V hardware
Luca Benini holds the chair of digital Circuits and systems at ETH Zurich and is Full Professor at the Universita di Bologna. Prof. Dr. Benini's research interests are in energy-efficient parallel computing systems, smart sensing micro-systems and machine learning hardware. He has published more than 1000 peer-reviewed papers and five books. He is an ERC-advanced grant winner, a Fellow of the IEEE, of the ACM and a member of the Academia Europaea. He is the recipient of the 2016 IEEE CAS Mac Van Valkenburg award and of the 2019 IEEE TCAD Donald O. Pederson Best Paper Award.
Torsten Hoefler
AI for High-Performance Climate and Earth Virtualization Engines
Machine learning presents a great opportunity for Climate simulation and research. We will discuss some ideas from the Earth Virtualization Engines summit in Berlin and several research results ranging from ensemble prediction and bias correction of simulation output, extreme compression of high-resolution data, and a vision towards affordable km-scale ensemble simulations. We will also discuss programming framework research to improve simulation performance. Specifically, our ensemble spread prediction and bias correction network applied to global data, achieves a relative improvement in ensemble forecast skill (CRPS) of over 14%. Furthermore, we demonstrate that the improvement is larger for extreme weather events on select case studies. We also show that our post-processing can use fewer trajectories to achieve comparable results to the full ensemble. Our ML-based compression method achieves data reduction from 300x to more than 3,000x and outperforms the state-of-the-art compressor SZ3 in terms of weighted RMSE and MAE. It can faithfully preserve important large scale atmosphere structures and does not introduce artifacts. When using the resulting neural network as a 790x compressed data loader to train the WeatherBench forecasting model, its RMSE increases by less than 2%. The three orders of magnitude compression democratizes access to high-resolution climate data and enables numerous new research directions. We will close by discussing ongoing research directions and opportunities for using machine learning for ensemble simulations and combine several machine learning techniques. All those methods will contribute to enabling km-scale global climate simulations.
Onur Mutlu
Memory-Centric Computing: Enabling Fundamentally Efficient Computers
Onur Mutlu is a Professor of Computer Science at ETH Zurich. He is also a faculty member at Carnegie Mellon University, where he previously held the Strecker Early Career Professorship. His current broader research interests are in computer architecture, systems, hardware security, and bioinformatics. A variety of techniques he, along with his group and collaborators, has invented over the years have influenced industry and have been employed in commercial microprocessors and memory/storage systems. He obtained his PhD and MS in ECE from the University of Texas at Austin and BS degrees in Computer Engineering and Psychology from the University of Michigan, Ann Arbor. He started the Computer Architecture Group at Microsoft Research (2006-2009), and held various product and research positions at Intel Corporation, Advanced Micro Devices, VMware, and Google. He received the IEEE Computer Society Edward J. McCluskey Technical Achievement Award, the ACM SIGARCH Maurice Wilkes Award, the inaugural IEEE Computer Society Young Computer Architect Award, the inaugural Intel Early Career Faculty Award, US National Science Foundation CAREER Award, Carnegie Mellon University Ladd Research Award, faculty partnership awards from various companies, and a healthy number of best paper or "Top Pick" paper recognitions at various computer systems, architecture, and hardware security venues. He is an ACM Fellow "for contributions to computer architecture research, especially in memory systems", IEEE Fellow for "contributions to computer architecture research and practice", and an elected member of the Academy of Europe (Academia Europaea).
Christoph Studer
Recycling Channel State Information for Self-Supervised Wireless Positioning
Christoph Studer was appointed Associate Professor in Integrated Information Processing at ETH Zurich in June 2020. Prof. Studer received his M.S. and Ph.D. degrees at the Department of Information Technology and Electrical Engineering (D-ITET) at ETH Zurich in 2006 and 2009, respectively. In 2005, he was a visiting researcher with the Smart Antennas Research Group at Stanford University. From 2009 to 2014, he was a postdoctoral researcher with the Communication Technology Laboratory at ETH Zurich and the Digital Signal Processing Group at Rice University in Houston, Texas. Dr. Studer's research interests include the design of digital very large-scale integration (VLSI) circuits, as well as wireless communications, optimization, signal and image processing, and machine learning. Dr. Studer received an ETH Medal for his M.S. thesis in 2006 and for his Ph.D. thesis in 2009. He received a two-year Swiss National Science Foundation fellowship for Advanced Researchers in 2011 and a US National Science Foundation CAREER Award in 2017. In 2016, Dr. Studer won a Michael Tien '72 Excellence in Teaching Award from the College of Engineering, Cornell University. He shared the Swisscom/ICTnet Innovations Award in both 2010 and 2013. Dr. Studer was the winner of the Student Paper Contest of the 2007 Asilomar Conf. on Signals, Systems, and Computers, received a Best Student Paper Award of the 2008 IEEE Int. Symp. on Circuits and Systems (ISCAS), and shared the best Live Demonstration Award at the IEEE ISCAS in 2013.
Anshumali Shrivastava
Beyond Matrix Multiplication: Algorithms for Scalable and Sustainable LLMs
Anshumali Shrivastava is an associate professor in the computer science department at Rice University. He is also the Founder of two startups ThirdAI Corp and xmad Corp. His broad research interests include probabilistic algorithms for resource-frugal deep learning. In 2018, Science news named him one of the Top-10 scientists under 40 to watch. He is a recipient of the National Science Foundation CAREER Award, a Young Investigator Award from the Air Force Office of Scientific Research, a Charles W. Duncan Jr. Outstanding Faculty Award from Rice, a young alumni award from IIT Kharagpur, a machine learning research award from Amazon, and a Data Science Research Award from Adobe. He has won numerous paper awards, including Best Paper Award at NIPS 2014, MLSys 2022, and Most Reproducible Paper Award at SIGMOD 2019. His work on efficient machine learning technologies has been covered by popular press including Wall Street Journal and New York Times.
Cong Shen
Transforming Demodulation
Cong Shen is an Associate Professor in the Department of Electrical and Computer Engineering of University of Virginia. He is also a member of the UVA Link Lab. He received the NSF CAREER award in 2022. His research interests span a number of interdisciplinary topics in machine learning, communications, and networking, with an emphasis on theoretical models, algorithms, and practical engineering applications. Some of my current research topics are - Federated learning and distributed/decentralized learning, Multi-armed bandits and reinforcement learning, Privacy-preserving machine learning, Wireless communications and networking.
Andreas Moshovos
Every Bit Matters: Enabling More Powerful Machine Learning Models
Andreas Moshovos along with his students has been answering the question “what is the best possible digital computation structure/software combination to solve problem X or to run application Y?” where “best” is a characteristic (or combination thereof) such as power, cost, complexity, etc. Much of his earlier work has been on high-performance processor and memory system design and it has influenced commercial designs. His more recent work has been on hardware/software acceleration methods for machine learning. He has been with the University of Toronto since 2000, but has also taught at Ecole Polytechnique Fédérale de Lausanne, Northwestern University, University of Athens, and the Hellenic Open University. He was the Scientific Director of the Canadian NSERC COHESA Research Network targeting machine learning optimizations, a consortium of 25+ research groups and industry partners.
Mohammad Alian
Acceleration of Compound AI Systems
Mohammad Alian serves as an Assistant Professor in the ECE Department at Cornell University. He completed his Ph.D. and MS degrees from UIUC (2020) and UW-Madison (2015), respectively. His research team is focused on redefining the data-delivery hierarchy of future data centers. Mohammad's research has garnered recognition, including best paper nominations at HPCA 2017 and MICRO 2018, an IEEE Micro Top Picks Honorable Mention in 2017, and an NSF CAREER award. He serves as a co-Principal Investigator and Broadening Participation Champion for the SRC/DARPA JUMP 2.0 ACE Center for Evolvable Computing, with the primary objective of revolutionizing distributed computing for the next decade.
Cristiano Malossi
AI 4 Enterprise Visual Inspection
Cristiano Malossi is Principal Research Scientist and Manager of the AI Automation Group at the IBM Research Laboratory in Zurich. Cristiano’s team owns the design, development, and productization of a scalable AI cloud service for detection of small and rare defects in large high-resolution images. In the earlier stages of his career at IBM, Cristiano has been responsible of the development of energy-aware computing algorithms, as part of the Exa2Green project. Later, between 2017 and 2020, Cristiano has been coordinator of the Open Transprecision Computing (OPRECOMP) project, with focus on low-power/low-energy computing paradigms, based on approximation and transprecision computing - a new computing paradigm created by the project that combines approximation and automation. Cristiano has also written and participated to many other EU projects, including VIMMP, IM-SAFE, and Romeo.
Christopher G. Brinton
Learning over Heterogeneous Networks: From Convergence Analysis to Intelligent Control.
I am the Elmore Rising Star Associate Professor of Electrical and Computer Engineering (ECE) at Purdue University, where I lead the ION research lab. Since joining Purdue, I have been fortunate to receive several external early career research awards, including NSF's CAREER Award, ONR's YIP (Young Investigator Program) Award, DARPA's YFA (Young Faculty Award), AFOSR's YIP Award, and Intel's RSA (Rising Star Faculty Award). Prior to joining Purdue, I was the Associate Director of the EDGE Lab and a Lecturer of Electrical Engineering at Princeton University, where I received my PhD. I also co-founded the big data startup company Zoomi Inc based on my research. I am also active in teaching. In addition to lecturing, I co-authored the book The Power of Networks: Six Principles That Connect Our Lives and have taught three Massive Open Online Courses (MOOCs) on networking.
Babak Falsafi
Sustainability of AI (in the datacenter) space
Babak Falsafi is a Professor in the School of Computer and Communication Sciences and the founder of EcoCloud, an industrial/academic consortium at EPFL investigating sustainable information technology. He is also the founding President of the Swiss Datacenter Efficiency Association with a platform and a label to quantify and certify energy efficiency and emissions in datacenter operation. He has made numerous contributions to server architecture since the 90s. His recent work on cloud-native CPUs laid the foundation for the first generation of Cavium ARM server CPUs, ThunderX. He is a recipient of an Alfred P. Sloan Research Fellowship, and a fellow of ACM and IEEE.
Ramtin Zand
Edge-Centric LLMs: Optimizing for High-Throughput on Embedded Devices
Dr. Ramtin Zand is the principal investigator of the Intelligent Circuits, Architectures, and Systems (iCAS) Lab at the University of South Carolina (USC), which collaborates with and is supported by multinational companies such as Intel, AMD, and Juniper Networks, along with local startups like Van Robotics. He has published over 70 papers and book chapters, earning recognitions including best paper awards from ACM GLSVLSI’18 and IEEE ISVLSI’21, First Place University Demo award at DAC'24, and a featured paper in IEEE Transactions on Emerging Topics in Computing. He has served on the technical program and organizing committees for several international conferences, including DAC, ICCAD, ISVLSI, GLSVLSI, and ISCAS. Dr. Zand is also a recipient of the prestigious NSF CAREER Award in 2024.
Abu Sebastian
Two Recent Innovations Towards Sustainable AI
Abu Sebastian is a Distinguished Scientist and technical manager at IBM Research – Zurich. He 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.
David Patterson
The Carbon Footprint of AI in the Cloud and the Edge Today
David Patterson received BA, MS, and PhD degrees from UCLA. He is a University of California Berkeley Pardee professor emeritus and a Google distinguished engineer. His most influential Berkeley projects likely were RISC and RAID. He received service awards for his roles as ACM President, Berkeley CS Division Chair, and CRA Chair and awards for his teaching. The most prominent of his seven books is Computer Architecture: A Quantitative Approach. He and his co-author John Hennessy shared the 2017 ACM A.M Turing Award and the 2022 NAE Charles Stark Draper Prize for Engineering. The Turing Award is often referred to as the “Nobel Prize of Computing” and the Draper Prize is considered a “Nobel Prize of Engineering.”
Evangelos Eleftheriou
Axlera’s AI Accelerator Technology
Evangelos Eleftheriou is the CTO and co-founder of Axelera AI. Previously, he held various research and management positions at IBM Research - Zurich. He has a PhD and a Master of Eng. in Electrical Engineering from Carleton University, Canada, and a BSc in Electrical & Computer Engineering from the University of Patras, Greece. His interests include AI, machine learning, and emerging computing paradigms such as neuromorphic and in-memory computing. He has authored over 250 publications and holds over 160 patents. Evangelos is an IEEE Fellow and co-recipient of the 2003 IEEE ComS Leonard G. Abraham Prize Paper Award, the 2005 Technology Award of the Eduard Rhein Foundation, and the 2009 IEEE Control Systems Technology Award and IEEE Transactions on Control Systems Technology Outstanding Paper Award. He was also appointed an IBM Fellow in 2005 and inducted into the IBM Academy of Technology. In 2016, he received an honoris causa professorship from the University of Patras, Greece, and in 2018, he was inducted into the US National Academy of Engineering as Foreign Member.
Hadi Esmaeilzadeh
Cross-Domain Multi-Acceleration: Powering Immersive Intelligence from Edge to Cloud
Dr. Hadi Esmaeilzadeh is awarded early tenure at UC San Diego, where he is the inaugural holder of Halicioglu Chair in Computer Architecture with the rank of full professor in Computer Science and Engineering. Before UC San Diego, he was an assistant professor in the School of Computer Science at Georgia Tech. There, he was the inaugural holder of the Allchin Family Early Career Professorship. Dr. Esmaeilzadeh obtained his Ph.D. in Computer Science and Engineering from the University of Washington in 2013. His Ph.D. work received the 2013 William Chan Memorial Best Dissertation Award. He is the founding director of the Alternative Computing Technologies (ACT) Lab, where his team is developing new technologies and cross-stack solutions to enable responsible immersive intelligence. His research has been recognized by four ISCA-50 Retrospective, highlighting the four of his papers’ significance over the past 25 years of ISCA from 1996 through 2020. Prof. Esmaeilzadeh’s work has also been recognized by four Communications of the ACM Research Highlights, four IEEE Micro Top Picks. Prof. Esmaeilzadeh received the IEEE Technical Committee on Computer Architecture (TCCA) “Young Architect” Award in 2018 and was inducted into the ISCA Hall of Fame in the same year. He has also received the Air Force Office of Scientific Research Young Investigator Award (2017), College of Computing Outstanding Junior Faculty Research Award (2017), Google Research Faculty Award (2018, 2016 and 2014), Qualcomm Research Award (2020, 2017, and 2016), Microsoft Research Award (2017 and 2016), Lockheed Inspirational Young Faculty Award (2016), and Samsung Research Award (2024 and 2023).