Keynote Speakers

Keynote Speakers

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Prof. Benini, Luca

Università di Bologna

Bio.: Luca Benini holds the chair of digital Circuits and systems at ETHZ and is Full Professor at the Università di Bologna. He received a PhD from Stanford University. His research interests are in energy-​efficient parallel computing systems, smart sensing micro-​systems and machine learning hardware. He is a Fellow of the IEEE, of the ACM, a member of the Academia Europaea and of the Italian Academy of Engineering and Technology. He is the recipient of several awards including the 2023 IEEE CS E.J. McCluskey Award, and the 2024 IEEE CS Open Source Hardware contribution Award.

Abstract: AI is accelerating into the generative era with increasing capabilities being "embodied" everywhere, from earbuds to cars to humanoid robots. Embodied AI needs to tackle major challenges in energy efficiency, safety, security, and real-time predictability, while curtailing computational complexity. In this talk I will focus on chip and system design for embodied AI, moving from ultra-low power AI-enhanced MCUs for smart wearables and nano-robots to large 3D-integrated systems-in-package for autonomous vehicles, humanoids and satellites. I will emphasize the strategic importance of AI-enhanced co-design flows and of an open-platform (hardware and software) approach to ensure fast innovation cycles as well as long term sustainability, safety and security.

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Prof. Yeo, Yee Chia

Deputy Chief Executive (Innovation & Enterprise), A*STAR

Bio.: Prof Yeo Yee Chia is currently the Deputy Chief Executive (Innovation & Enterprise), A*STAR. He provides strategic leadership and oversight of the Innovation & Enterprise Group and National Platforms. He orchestrates A*STAR's industry approach and priorities to ensure that A*STAR's translational activities achieve significant national impact and outcomes.
Prior to this, he was Assistant Chief Executive (Innovation & Enterprise), where he strengthened A*STAR's industry partnerships and commercialisation efforts. Prof Yeo also held various leadership roles in A*STAR, including Executive Director (IP Management) and Executive Director of the Science & Engineering Research Council (SERC). He leads substantial research efforts at A*STAR's Institute of Microelectronics, working on frontier microelectronic devices and key challenges in semiconductor technology. He plays a significant role in Singapore's Future of Microelectronics initiative to steer next-generation microelectronics strategy.
Prof Yeo is a renowned scholar in academia and a technology leader in the semiconductor industry, with expertise in transistor architecture, device modelling and simulation, materials, tools and processing technologies.
He spent 10 years at Taiwan Semiconductor Manufacturing Company (TSMC). He was Director of Research and Development (R&D) at TSMC where he led organisations spanning research, pathfinding, and development. He co-developed TSMC's industry-leading 7 nm, 5 nm, and 3 nm technologies, which has impacted the world in areas such as high performance computing, artificial intelligence, and consumer electronics.
In academia, he is Professor of Electrical and Computer Engineering, National University of Singapore (NUS), where he was an award-winning, high impact researcher with an outstanding track record of achievements. He published over 700 research papers and is an inventor of 289 US patents. 44 PhD students graduated under his tutelage.
He received his PhD and MS degrees in Electrical Engineering and Computer Sciences from the University of California, Berkeley, and MEng and BEng degrees in Electrical Engineering from NUS.

Abstract: TBA

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Mr. Kengeri, Subramani

Corporate Vice President and GM, Systems to Materials, Applied Materials

Bio.: Subramani (Subi) Kengeri is the Corporate Vice President and General Manager at Applied Materials, leading the Systems-to-Materials group since joining in 2020. He has led world-wide teams in technical and executive roles at Globalfoundries, TSMC, and Texas Instruments. He holds over 50 U.S. patents, and has delivered more than 100 invited talks and interviews.

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Prof. Yeo, Seng Kiat

Singapore University of Technology and Design

Bio.: Professor Kiat Seng YEO, (PhD, PPA, PBS, FSAEng, FSNAS, FCAE, FIEEE, FAAET, FAIIA, FAAIA) received his BEng and PhD degrees in EE from Nanyang Technological University (NTU), Singapore. He has 37 years of experience in industry, academia, start-ups and consultancy. Currently, he is Advisor for Global Partnerships (President’s Office) and Director for Innovation and Enterprise (China) at the Singapore University of Technology and Design (SUTD) and Distinguished Professor at Tianjin University. Prof. Yeo is a widely known authority in low-power RF/mm-wave IC design and a recognized expert in CMOS technology. He was the Founding Chairman of the University Research Board, Founding Chairman of the Board of Graduate Studies, Member of the Academic Council, Associate Provost for Research, Founding Associate Provost for Graduate Studies, and Founding Associate Provost for International Relations at SUTD. Before his appointment at SUTD, he was Associate Chair (Research), Head of the Division of Circuits and Systems, and Founding Director of VIRTUS of the School of Electrical and Electronic Engineering at NTU. He has secured over S$70M of research grants as PI, published 14 books (4 Amazon Best Sellers), 7 book chapters, 700+ journal and conference papers, and holds 55 patents (27 US patents). Professor Yeo holds/held key positions in many international conferences as Advisor, General Chair and Technical Chair. He is one of the 7 “double academicians” of the Singapore Academy of Engineering and the Singapore National Academy of Science. He is also a Foreign Fellow of the Canadian Academy of Engineering and Fellows of the ASEAN Academy of Engineering and Technology, the Asia-Pacific Artificial Intelligence Association, the International Artificial Intelligence Industry Alliance, and the IEEE. He received 2 National Day Awards from the President of Singapore in 2009 and 2020 and awarded the Nanyang Alumni Achievement Award by NTU in 2009. He is the principal author of Integrated Circuit Design Research Ranking for Worldwide Universities 2008 and World University Research Rankings (WURR) 2020. Professor Yeo was recognized among the Top 2% Scientists Worldwide by Stanford University from 2020 to 2025, World’s AI Top Scientist by the International Artificial Intelligence Industry Alliance in 2023, and Top Scholar by ScholarGPS from 2023 to 2025.

Abstract: The rise of artificial intelligence (AI) has been more prominent in the last few years. It has and will continue to shape and accelerate the growth of both the electronic design automation (EDA) and the semiconductor industry markets. For example, the EDA market is expected to increase from US$16 billion in 2026 to over US$22 billion by 2030. During the same period, the semiconductor industry market is projected to grow from US$820 billion to over US$1 trillion.
Traditionally, the growth of both EDA and semiconductor industry is dominated by the 4 C’s: Computers, Communications, Consumers, and Cars. But its growth will now come from the 4 I’s: Intelligence, Integration, Innovation, and Interdiscipline. The combination of these I’s and C's will result in more ICs. Therefore, in addition to new energy, drones and smart cars, ICs will also be widely used in the human body, robots, future communications, biomedical, aerospace and many other fields. In the foreseeable future, there will be more microchips in the human body than in any electronic devices. At that time, humans will become more robotic, and robots will become more human.
As AI and IC continue to evolve, it is important to know the forces that have driven it along its historical trajectory, and to discover how much further it could go. What is the next big thing? How is it going to affect us? This keynote will attempt to answer these questions.

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Mr. Chan, Don

Vice president, Research & Development
Cadence Design Systems, Inc.

Bio.: Don Chan is the vice president of R&D at Cadence Design Systems. He received his MS degree in electrical engineering from Santa Clara University in 1990. From 1986-1989, he was developing SPICE models. From 1989-1992, he was as ASIC Design Engineer at Fujitsu Microelectronics. From 1993-2018, Don held various leadership positions in both applications engineering and R&D at Synopsys. From 2018-present, Don is the VP of R&D collaborating with the world’s #1 foundry in developing the most advanced nodes. In addition, Don is also the product owner for the frontend products at Cadence which includes Conformal, Modus and Stratus. Don has given many keynote speeches in many conferences in Asia. Don is also currently a Lecturer at Santa Clara University.

Abstract: Electronic Design Automation (EDA) is the enabler for today's semiconductor growth. Moore's Law has been driving the semiconductor industry for the past several decades. Correspondently, EDA has increased the productivity of engineers by more than 10,000X over the decades. In this talk, we discussed how EDA together with design technology co-optimization has advanced beyond Moore's law and transistor scaling. With transistor scaling becoming increasingly challenging, the future of designs will approach 3D in the next few years. Driven by high performance computing (HPC) and AI applications, systems will be comprised of heterogenous chiplets with different process nodes. In the era of AI, design methodology will become AI-assisted design methodology and scripts will be driven by agentic AI and agents. The future of EDA research lies in agentic workflows.

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Prof. Parhi, Keshab

University of Minnesota

Bio.: Keshab K. Parhi received the B.Tech. degree from the Indian Institute of Technology (IIT), Kharagpur, in 1982, the M.S.E.E. degree from the University of Pennsylvania, Philadelphia, in 1984, and the Ph.D. degree from the University of California, Berkeley, in 1988. He has been with the University of Minnesota, Minneapolis, since 1988, where he is currently the Erwin A. Kelen Chair and a Distinguished McKnight University Professor in the Department of Electrical and Computer Engineering.
He has published over 750 papers, is the inventor of 36 patents, and has authored the textbook VLSI Digital Signal Processing Systems: Design and Implementation (Wiley, 1999). His current research addresses VLSI architecture design of artificial intelligence and machine learning systems, signal processing systems, hardware security, quantum information science, and data-driven neuroengineering and neuroscience with applications to neuro-psychiatric disorders.
Dr. Parhi is the recipient of numerous awards including the 2003 EEE Kiyo Tomiyasu Technical Field Award; and the 2017 Mac Van Valkenburg award, and the 2012 Charles A. Desoer Technical Achievement award, from the IEEE Circuits and Systems Society. He served as the Editor-in-Chief of the IEEE Trans. Circuits and Systems, Part-I during 2004 and 2005, and currently serves as the Editor-in-Chief of the IEEE Circuits and Systems Magazine. He is a Life Fellow of IEEE, a Fellow of the American Association for the Advancement of Science (AAAS), the Association for Computing Machinery (ACM), the American Institute of Medical and Biological Engineering (AIMBE), and the National Academy of Inventors (NAI).

Abstract: The field of artificial intelligence (AI) has taken a tight hold on broad aspects of society, industry, business, and governance in ways that dictate the prosperity and might of the world's economies. The AI market size is projected to grow from $189 billion in 2023 to $4.8 trillion by 2033. Currently, AI is dominated by large language models (LLMs) that exhibit linguistic and visual intelligence. However, training these models requires a massive amount of data scraped from the web as well as large amounts of energy (50-60 GWh to train GPT-4). Despite these costs, these models often hallucinate, a characteristic that prevents them from being deployed in critical application domains. In contrast, the human brain consumes only 20W of power. What is needed is the next level of AI evolution in which lightweight domain-specific multimodal models with higher levels of intelligence can reason, plan, and make decisions in dynamic environments with real-time data and prior knowledge, while learning continuously and evolving in ways that enhance future decision-making capability. This will define the next wave of AI, progressing from today's large models, trained with vast amounts of data, to nimble energy-efficient domain-specific agents that can reason and think in a world full of uncertainty. To support such agents, hardware will need to be reimagined to allow energy efficiencies ≥ 1000X over the state of the art. Such a vision of future AI systems is presented in this talk. The talk is based on collaborative work with colleagues as presented in arXiv 2510.22052.

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Prof. Chakrabarty, Krishnendu

Fulton Professor of Microelectronics, Arizona State University

Bio.: Prof. Krishnendu Chakrabarty is the Fulton Professor of Microelectronics in the School of Electrical, Computer and Energy Engineering at Arizona State University (ASU). He is also the CTO of the SWAP Hub for the Department of War Microelectronics Commons (https://microelectronics.asu.edu/southwest-advanced-prototyping-hub/), and Director of the ASU Center for Semiconductor Microelectronics (ACME, http://acme.asu.edu). Before moving to ASU, he was the John Cocke Distinguished Professor and Chair of Electrical and Computer Engineering at Duke University. Prof. Chakrabarty is a recipient of the Humboldt Research Award from the Alexander von Humboldt Foundation, Germany, the IEEE Transactions on CAD Donald O. Pederson Best Paper Award, the IEEE Transactions on VLSI Systems Prize Paper Award, the ACM Transactions on Design Automation of Electronic Systems Best Paper Award, and over a dozen best paper awards at major conferences. He is also a recipient of the IEEE Computer Society Technical Achievement Award, the IEEE Circuits and Systems (CAS) Society Charles A. Desoer Technical Achievement Award, the IEEE CAS Society Vitold Belevitch Award, the Semiconductor Research Corporation (SRC) Technical Excellence Award, the SRC Aristotle Award, the SRC Innovation Award, the IEEE-HKN Asad M. Madni Outstanding Technical Achievement and Excellence Award, and the IEEE Test Technology Technical Council Bob Madge Innovation Award. He is a Research Ambassador of the University of Bremen (Germany) and he was a Hans Fischer Senior Fellow at the Institute for Advanced Study, Technical University of Munich, Germany during 2016-2019. He is a 2018 recipient of the Japan Society for the Promotion of Science (JSPS) Invitational Fellowship in the “Short Term S: Nobel Prize Level” category. He is also a recipient of the Distinguished Alumnus Award from the Indian Institute of Technology, Kharagpur. Prof. Chakrabarty is a Fellow of ACM, IEEE, and AAAS, and a Golden Core Member of the IEEE Computer Society. He is a Fellow of the National Academy of Inventors.

Abstract: This talk will focus on how we can recover from defects in fanout wafer-level packaging (FOWLP) and hybrid bonding. Defects in FOWLP can arise from the coefficient of thermal expansion mismatch, warpage, die shift, and post-molding protrusion, causing misalignment and imperfect bonding during redistribution layer (RDL) buildup. As a result, high-density back-end-of-line (BEOL) interconnects, RDLs, and through-mold vias are susceptible to warpage-induced stress, strain, and deformation. Hybrid bonding interfaces are vulnerable to nanoscale voids, contamination, and alignment-induced opens or shorts. The speaker will present an analysis of these defects, fault models, and impact of these defects on logic functionality and timing. The presentation will showcase recent breakthroughs in built-in self-test and repair for FOWLP and hybrid bonding, translating cutting-edge academic research into practical solutions.

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Prof. Alioto, Massimo

National University of Singapore

Bio.: Massimo Alioto is Provost's Chair Professor at the ECE Department of the National University of Singapore, where he leads the Green IC group, the Integrated Circuits and Embedded Systems area, and the FD-fAbrICS center on intelligent&connected systems. Previously, he held positions at the University of Siena, Intel Labs - CRL (2013), University of Michigan - Ann Arbor (2011-2012), University of California - Berkeley (2009-2011), EPFL - Lausanne.
He is (co)author of 400 publications on journals and conference proceedings, and four books with Springer (with two more coming). His primary research interests include ultra-low power and self-powered systems, green computing, circuits for machine intelligence, hardware security, and emerging technologies.
He was the Editor in Chief of the IEEE Transactions on VLSI Systems and Deputy Editor in Chief of the IEEE Journal on Emerging and Selected Topics in Circuits and Systems. He was the Chair of the Distinguished Lecturer Program for the IEEE CAS Society, and was a Distinguished Lecturer for the SSC and CAS Society. Previously, Prof. Alioto was the Chair of the “VLSI Systems and Applications” Technical Committee of the IEEE Circuits and Systems Society (2010-2012). He served as Guest Editor of numerous journal special issues (JSSC, TCAS-I, JETCAS…), Technical Program Chair of several IEEE conferences (ISCAS, SOCC, PRIME, ICECS), and TPC member (ISSCC, ASSCC). His research group contribution has been recognized through various best paper awards (e.g., ISSCC), and in the ten technological highlights of the TSMC annual report, among the others. Prof. Alioto is an IEEE Fellow.

Abstract: Artificial intelligence (AI) has pervasively augmented the capabilities of today’s silicon systems with an unprecedented level of context awareness and physical signal insights. For this reason, AI accelerator always-on operation is becoming a fundamental and non-negotiable requirement, which conflicts with the high computational intensity of common models and neural networks. This requires fundamental advances in energy efficiency of always-on AI accelerator architectures to achieve a much more favorable tradeoff between power (or energy/inference) and quality of insights (e.g., accuracy) from algorithm to architecture and circuit. As further challenge, AI accelerators are being progressively pushed into silicon systems with extremely limited power budget down to battery-powered and even purely-harvested systems, which make the embedment of always-on AI even more challenging.
In this keynote, recent directions to drastically improve the energy efficiency of AI always-on accelerators are presented and exemplified with a wide range of silicon demonstrations with state-of-the-art low consumption. Examples cover a wide range of AI model classes, use cases and sensing modalities such as CNNs, transformers and vision systems. The underlying design principles are illustrated for a very wide range of power budgets down to µWs, enabling always-on operation even without any form of system energy storage for extremely small form factors and low cost. As common thread, a clear pathway to make AI truly ubiquitous in next-generation silicon systems is unraveled, calling upon our entire research and chip design community to join forces in making it feasible at scale.

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Prof. Takahashi, Atsushi

Institute of Science Tokyo

Bio.: Atsushi TAKAHASHI received his B.E., M.E., and D.E. degrees in electrical and electronic engineering from Tokyo Institute of Technology, Tokyo, Japan, in 1989, 1991, and 1996, respectively. He had been with the Tokyo Tech as a Research Associate from 1991 to 1997, and as an Associate Professor from 1997 to 2009 and from 2012 to 2015, and as a Professor from 2015. He had been with the Osaka University as an Associate Professor from 2009 to 2012. He is currently a Professor with Department of Information and Communications Engineering, School of Engineering, Tokyo Institute of Technology. He served as the TPC Chair of ASP-DAC 2018, the General Chair of ASP-DAC 2023, and he is currently a vice chair of Steering Committee of ASP-DAC. He also served as the chair/co-chair/TPC member of top conferences such as ASP-DAC, ISPD, ICCAD, DATE and VLSI-DAT. He was a Board of Governors of IEEE CASS from 2015 to 2019, the chair of All Japan Joint Chapter of IEEE CEDA from 2015 to 2016, the chair of Japan Joint Chapter of IEEE CASS from 2020 to 2021, and the President of Engineering Sciences Society of IEICE in 2021. His research interests include in VLSI layout design and combinational algorithms. He is a fellow of IEICE, and a senior member of IEEE and IPSJ, and a member of ACM.

Abstract: Routing has been extensively studied as one of key issues in physical design area of the electric design automation from its emergence, and various routing algorithms, tools and systems have been developed and used in practice. However, research and development of routing is still required since new constraints and objectives forced on routing arise continuously from new technologies and innovative design flow. In this talk, various routing algorithms from classical one to advanced one will be discussed.

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Prof. Wunderlich, Hans-Joachim

Professor Emeritus of the University Stuttgart

Bio.: Hans-Joachim Wunderlich is a professor emeritus of the University of Stuttgart and Life Fellow of IEEE. He received his diploma degree in mathematics from the University of Freiburg, Germany, in 1981 and his Dr. rer. nat. (Ph.D.) from the University of Karlsruhe in 1986. Since 1991, he has been a full professor. From 2002 to 2018, he served as the director of the Institute of Computer Architecture and Computer Engineering at the University of Stuttgart, Germany. He has been an associate editor for various international journals and involved in the organization of a variety of IEEE conferences on the design, testing, and fault tolerance of electronic systems. He has published 17 books and book chapters and more than 330 reviewed scientific papers in journals and conferences. His research interests include testing, reliability, fault tolerance, and design automation of microelectronic systems.

Abstract: During the last few years, silent data corruption (SDC) received increasing attention, and nearly all the players operating AI-Giga-Factories (Artificial Intelligence) reported a certain amount of semiconductor chips computing erroneous results. This phenomenon is actually not as new as it appears, but it is publicly noticed, since nowadays a single customer may run several 100 thousands of chips, and failures are not any more a one-time exception rather than a pattern.
Nearly two decades ago, automotive companies integrating millions of devices in their produced cars in total, made the same observation and took counteractions. With the increased scaling and variability, the classic view of manufacturing test, fault coverage and test escapes does not hold any more as test is now an indeterministic process.
Even if a test procedure will lead to a complete test coverage for nominal circuits instances, variations may create an instance where the test is incomplete. One reason is the exponentially increasing number of possibly critical paths, and not all of them can be tested. The other reason is in the fact that variations may invalidate a test even for severe gross faults. The problem is aggravated by adding voltage and temperature to the process variations leading to an intractable number of distributions.
Since not only defective but also weak devices have to be identified during test, the challenges become even harder. Weak devices may not fail the structural and functional manufacturing test but lead to early life failures severely impacting reliability. Hence, extra-functional properties have to be observed including leakage, power-consumption, temperature and behavior under varying conditions likes voltages and temperatures. These hidden faults mostly lead to rare timing events, and structural scan based or self-testable test techniques are still necessary but not anymore sufficient. They have to be complemented by system level test methods which in turn may require extremely long initialization sequences.
In the realm of automotive electronics these limitations of manufacturing tests are widely known, and international standards define requirements for concurrent and periodic online test or even fault tolerance. Today, general processor design, GPUs and applications in High Performance Computing (HPC) and AI-Giga-Factories are following this trend. Silicon Lifecycle Management (SLM) covers all the phases from the design, prototyping, bring-up, operational phase and wear-out, and especially the last three phases rely on comprehensive collecting all necessary observables by telemetry. The term telemetry denotes not only the collection of data from sensors, monitors, BIST registers, as well as test and fault tolerance actions but also the evaluation, classification and prediction of the system state more and more by means of embedded AI.
First step is always the analysis of the root causes of failures and diagnosis of faulty systems, and the powerful capabilities of today's systems allow the implementation of AI-based analysis algorithms on chip, in edge-computing or in the cloud. Combining high quality manufacturing test and telemetry leads to the required level of reliable computing.

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Prof. Gupta, Puneet

Professor of University of California, Los Angeles

Bio.: Puneet Gupta received the B.Tech. degree in electrical engineering from the Indian Institute of Technology Delhi, New Delhi, India, in 2000, and the Ph.D. degree from the University of California at San Diego, San Diego, CA, USA, in 2007. He is currently a Faculty Member with the Electrical and Computer Engineering Department, University of California at Los Angeles. He Co-Founded Blaze DFM Inc., Sunnyvale, CA, USA, in 2004 and served as its Product Architect until 2007. He has authored over 200 papers, 18 U.S. patents, a book and two book chapters in the areas of design-technology co-optimization as well as variability/reliability aware architectures. Dr. Gupta is an IEEE Fellow and was a recipient of the NSF CAREER Award, the ACM/SIGDA Outstanding New Faculty Award, SRC Inventor Recognition Award, and the IBM Faculty Award. He has led the multi-university IMPACT+ Center which focused on future semiconductor technologies. He currently leads the System Benchmarking theme within the SRC CHIMES JUMP 2.0 center.

Abstract: TBA

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