Bio: Karin Strauss is a Senior Principal Research Manager at Microsoft Research and an Affiliate Professor at the University of Washington. She co-leads the Molecular Information System Laboratory, working on using molecules, including DNA, to benefit the IT industry. Her background is in computer architecture and systems. Her research interests include environmental sustainability, DNA data storage, emerging storage technologies more broadly, and scaling of computation and storage. Selected as one of the "100 Most Creative People in Business in 2016" by Fast Company Magazine, she is also a co-recipient of the ACM SIGARCH Maurice Wilkes award for work on the DNA Data Storage project. She got her PhD from the Department of Computer Science at the University of Illinois, Urbana-Champaign in 2007. 

Abstract: Sustaining digital data storage is an increasingly challenging proposition for the storage industry. This talk will cover an alternative approach: going directly to the molecular level. Although we have yet to achieve scalable, general-purpose storage and molecular computation, there are areas of IT in which a molecular approach shows growing promise. In this talk, I will explain how molecules, specifically synthetic DNA, can store digital data and perform certain types of special-purpose computation, such as large-scale similarity search, by leveraging and further innovating tools already developed by the biotechnology industry. I will conclude with remarks on how molecular technologies, materials design, and computational tools can improve environmental sustainability of the IT industry and beyond. 

Bio: Lieven Eeckhout (PhD 2002) is a Senior Full Professor at Ghent University, Belgium. His research interests include computer architecture and the hardware/software interface, with specific emphasis on performance evaluation and modeling, dynamic resource management, CPU/GPU microarchitecture, and sustainability. He is the recipient of the 2017 ACM SIGARCH Maurice Wilkes Award and the 2017 OOPSLA Most Influential Paper Award, and he was elevated to IEEE Fellow in 2018 and ACM Fellow in 2021. He served as Program Chair for ISCA 2020, HPCA 2015, CGO 2013 and ISPASS 2009; Editor-in-Chief for IEEE Micro (2015-2018); and technical program committee member for 50+ computer architecture conferences. He is the recipient of five European Research Council (ERC) grants, including a Starting Grant, an Advanced Grant and three Proof-of-Concept Grants. 

Abstract: Sustainability and climate change is a major challenge for our generation. In this talk I will argue that sustainable development requires a holistic approach and involves multi-perspective thinking. Applied to computing, sustainable development means that we need to consider the entire environmental impact of computing, including raw material extraction, component manufacturing, product assembly, transportation, use, repair/maintenance, and end-of-life processing (disassembly and recycling/reuse). Analyzing current trends reveals that the embodied footprint is, or will soon be, more significant compared to the operational footprint. I will present a simple, yet insightful, first-order model to assess and reason about the sustainability of computer systems in light of the inherent data uncertainty. Applying the model to a variety of case studies illustrates what computer architects and engineers can and should do to better understand the sustainability impact of computing, and to design sustainable computer systems. 

Bio: David Atienza is a professor of Electrical and Computer Engineering, and leads both the Embedded Systems Laboratory (ESL) and the new EcoCloud Sustainable Computing Center at EPFL, Switzerland. He received his M.Sc. and Ph.D. degrees in Computer Science and Engineering from UCM (Spain) and IMEC (Belgium). His research interests include system-level design methodologies for high-performance multi-processor system-on-chip (MPSoC) and low power Internet-of-Things (IoT) systems, including edge AI architectures for wearables and IoT systems as well as thermal-aware designs for MPSoCs and many-core servers. He is a co-author of more than 400 papers, two books, and has 14 licensed patents in these topics. He served as DATE General Chair and Program Chair, and is currently Editor-in-Chief of IEEE TCAD. Among others, Dr. Atienza has received the ICCAD 2020 10-Year Retrospective Most Influential Paper Award, the 2018 DAC Under-40 Innovators Award, an ERC Consolidator Grant in 2016, the 2013 IEEE CEDA Early Career Award, and the 2012 ACM SIGDA Outstanding New Faculty Award. He is a Fellow of IEEE, a Fellow of ACM, served as IEEE CEDA President (period 2018-2019), and he is currently the Chair of the European Design Automation Association (EDAA).

Abstract: Internet of Things (IoT) is the next frontier of innovation where our everyday objects are connected in ways that improve our lives and can transform industries, in particular healthcare and wearables. In this keynote, Prof. Atienza will discuss new approaches to effectively co-design the next generation of edge AI computing systems by taking inspiration from how biological computing systems operate. This new trend of bioinspired system co-design approaches combines a new open-source eXtended and Heterogeneous Energy Efficient hardware Platform (called x-HEEP) while operating ensembles of neural networks to improve robustness of the outputs and minimizing memory and computation resources. Consequently, this advocated next generation of IoT systems benefit from critical biological concepts, such as in-memory computing or run-time reconfigurable coarse-grained accelerators, to enable a minimal energy consumption according to the required precision of the target application. Moreover, these new bioinspired systems can go one step beyond current biological systems. In particular, they can easily collaborate through federated learning to gradually help each other improve their outcomes in different real-life operating conditions.

Bio: Prof. Ayse K. Coskun is a full professor at Boston University (BU) at the Electrical and Computer Engineering Department, where she leads the Performance and Energy Aware Computing Laboratory (PeacLab) to solve problems towards making computer systems more intelligent and energy-efficient. Coskun is also the Director of the Center for Information and Systems Engineering (CISE). Coskun’s research interests intersect design automation, computer systems, and architecture. Her research outcomes are widely recognized and culminated in several technical awards, including the NSF CAREER Award, the IEEE CEDA Ernest Kuh Early Career Award, and an IBM Faculty Award. Coskun has been an avid collaborator of industry (collaborators include IBM TJ Watson, Oracle, AMD, Intel, and others) and received several patents during her time at Sun Microsystems (now Oracle). Her research team has released several impactful software artifacts and tools to the open-source community. Following her passion in increasing diversity in STEM fields, Coskun has regularly participated in outreach programs at BU and also founded a new forum called “Advancing Diversity in EDA” (DivEDA). She currently serves as the Deputy Editor-in-Chief of the IEEE Transactions on Computer Aided Design. Coskun received her PhD degree in Computer Engineering from University of California San Diego and her BS degree in Microelectronics Engineering from Sabanci University in Turkey.

Abstract: Today’s large-scale computer systems that serve high performance computing and cloud face challenges in delivering predictable performance, while maintaining efficiency, resilience, and security. Much of computer system management has traditionally relied on (manual) expert analysis and policies that rely on heuristics derived based on such analysis. This talk will discuss a new path on designing “automated analytics” methods for large-scale computer systems and how to make strides towards a longer term vision where computing systems are able to self-manage and improve. Specifically, the talk will first cover how to systematically diagnose root causes of performance “anomalies”, which cause substantial efficiency losses and higher cost. Second, it will discuss how to identify applications running on computing systems and discuss how such discoveries can help reduce vulnerabilities and avoid unwanted applications. The talk will also highlight how to apply machine learning in a practical and scalable way to help understand complex systems, demonstrate methods to help standardize study of performance anomalies, discuss explainability of applied ML methods in the context of computer systems, and point out future directions in automating computer system management.