Keynote Speakers

ACM e-Energy Keynotes

Tamar Eilam

IBM Fellow and Chief Scientist for Sustainable Computing, IBM Research

Sustainable AI: Balancing Performance and Planet

Abstract: The rapid advancements in Artificial Intelligence (AI) have revolutionized various domains, such as health, transportation, and language, and is also a powerful tool to fight climate change. However, this progress comes with a significant environmental cost. AI models consume tremendous amounts of energy, raising concerns about their sustainability. How can we reap the benefits of AI while working to reduce its carbon cost? In this talk we will first review the fundamentals of Sustainable Computing research, and then turn our attention to AI and its unique energy efficiency challenge. We advocate an approach that treats models as products and examines energy efficiency holistically across its lifecycle.

Biography: Dr. Tamar Eilam is an IBM Fellow and Chief Scientist for Sustainable Computing in the IBM T. J. Watson Research Center, New York. Tamar is leading research aiming at drastically reducing the carbon footprint associated with computing across infrastructure, systems, and software, data and AI. Tamar complete a Ph.D. degree in Computer Science in the Technion, Israel, in 2000. She joined the IBM T.J. Watson Research Center in New York as a Research Staff Member that same year. She was recognized as an IBM Fellow in 2014.

Zhao Yang Dong

Chair Professor of Electrical Engineering and Head of Department, Department of Electrical Engineering, City University of Hong Kong

Real-Time Carbon Emission Measurement Based on Non-Intrusive Load Monitoring

Abstract: Accurate carbon emission accounting is crucial for combatting climate change. We propose an innovative real-time estimation framework tailored for industrial parks, addressing limitations of current methods. Our data-driven approach integrates Scope 1 and Scope 2 emissions, utilizing non-intrusive load monitoring (NILM) algorithms and real-time meter data. Scope 1 emissions are calculated with an advanced NILM algorithm, achieving 93.4% accuracy. Scope 2 emissions are computed using precise factors and locational data. Validation within a four-factory industrial park shows a 0.44% estimation error over one year, surpassing IPCC method by 2.32%. Real-time estimation enables proactive emissions tracking, empowering informed decision-making for sustainable practices.

Biography: Prof. Z.Y. Dong is currently Chair Professor of Electrical Engineering and Head of Department of Electrical Engineering, City University of Hong Kong. His previous roles include Singapore Power Group (SPG) Endowed Professor of Power Engineering, and Co-Director of SPG-NTU Joint Lab at Nanyang Technological University, Singapore, SHARP Professor with the University of New South Wales (UNSW), Australia.He was the inaugural Director of UNSW Digital Grid Futures Institute, Director of ARC Research Hub for Integrated Energy Storage Solutions, Ausgrid Chair Professor and Director of Ausgrid Centre for Intelligent Electricity Networks providing R&D support for the AU$500m Smart Grid, Smart City national demonstration project of Australia. His research interest includes power system planning, load modelling, smart grid, smart cities, energy market, renewable energy and its grid connection, and computational methods and their application in power system analysis. He has been serving as editor/associate editor of several IEEE transactions and IET journals. He is a Fellow of IEEE.

Ravishankar K. Iyer

George and Ann Fisher Distinguished Professor of Engineering, Departments of Electrical and Computer Engineering, Computer Science and Coordinated Science Laboratory, University of Illinois at Urbana-Champaign

When Green Computing Meets Performance and Resilience SLOs

Abstract: This talk addresses the urgent need to transition to global net-zero carbon emissions by 2050 while retaining the ability to meet joint performance and resilience objectives. The focus is on the computing infrastructures, such as hyperscale cloud datacenters, that consume significant power, thus producing increasing amounts of carbon emissions. Our goal is to (a) optimize the use of green energy sources (e.g., solar/wind energy), which is desirable but currently expensive and relatively unstable, and (b) continuously reduce the use of fossil fuels, which have a lower cost but a significant negative societal impact. Meanwhile, cloud datacenters strive to meet their customers’ requirements, e.g., service-level objectives (SLOs) in application latency or throughput, which are impacted by infrastructure resilience and availability. We propose a scalable formulation that combines sustainability, cloud resilience, and performance as a joint optimization problem with multiple interdependent objectives to address these issues holistically. Given the complexity and dynamicity of the problem, machine learning (ML) approaches, such as reinforcement learning, are essential for achieving continuous optimization. Our work highlights the challenges of green energy instability which necessitates innovative ML-centric solutions across heterogeneous infrastructures. These aim to manage the transition towards green computing. Underlying the ML-centric solutions must be methods to combine classic system resilience techniques with innovations in real-time ML resilience (not addressed heretofore). We believe that this approach will not only set a new direction in the resilient, SLO-driven adoption of green energy but also enable us to manage future sustainable systems in ways that were not possible before.

Biography: Ravishankar K. Iyer is the George and Ann Fisher Distinguished Professor of Engineering at the University of Illinois at Urbana-Champaign with joint appointments in the Departments of Electrical and Computer Engineering (ECE), and Computer Science (CS) in the Coordinated Science Laboratory (CSL), the National Center for Supercomputing Applications (NCSA), and the Carl R. Woese Institute for Genomic Biology. He was the founding chief scientist of the Information Trust Institute at UIUC—a campus-wide research center addressing security, reliability, and safety of critical infrastructures. His work has been recognized by several awards from the IEEE, ACM, AIAA among others. He leads the DEPEND Group at CSL/ECE at Illinois, with a multidisciplinary focus on systems and ML that combine deep measurement-driven analytics and applications spanning performance, resilience and the security of critical infrastructures. Prof. Iyer is a Fellow of the IEEE, ACM and AAAS.