2023 Visionary Speakers

Systems 2030 – What’s Needed to Succeed in the Next Decade of Design without Resorting to Human Cloning

Monday, July 10, 2023

With the accelerating pace of semiconductor complexity, and the increasing software and semiconductor content in systems, traditional EDA levels of productivity increases are insufficient. As electronic system expand to literally hold our lives in their virtual hands, good-enough no longer is.  What is needed are solutions across the ecosystem that provide orders of magnitude improvements to productivity, allow new engineers to become experts in very short periods of time, facilitate cross domain collaboration and optimization, and minimize the design space that needs to be explored to ensure systems operate correctly.

In this talk we will explore the past, present and future of complex semiconductor systems design.

ABOUT: Joseph Sawicki is a leading expert in IC nanometer design and manufacturing challenges. Formerly responsible for Mentor’s industry-leading design-to-silicon products, including the Calibre physical verification and DFM platform and Mentor’s Tessent design-for-test product line, Sawicki now oversees all business units in the Siemens EDA IC segment. Sawicki joined Mentor Graphics in 1990 and has held previous positions in applications engineering, sales, marketing, and management. He holds a BSEE from the University of Rochester, an MBA from Northeastern University’s High Technology Program, and has completed the Harvard Business School Advanced Management Program.

Driving Engineering Simulation and Design with AI/ML

Tuesday, July 11, 2023

Traditionally, engineered products were designed with mechanical and electrical CAD tools, simulated and validated for correctness with CAE tools, prototypes were fabricated and tested, and products were then manufactured at scale in factories. This process required long product cycles often spanning years to build a new product. Today, virtually unlimited computing and storage available from the cloud is available for generative design to explore 10,000 design choices in near real-time, verify these products accurately through simulation (eliminating the need to build physical prototypes) and manufacture the products using additive manufacturing and factory automation. In the past, simulation tools were used to model specific, solitary physics such as mechanical structures, fluid dynamics, or electromagnetic interactions by solving second order partial differential equations using numerical methods. Today, simulation tools solve multi-physics problems (fluid-structure-electromagnetics interactions) at scale using the most complex solvers. We will explore the use of AI, Machine Learning and Deep Learning to accelerate these engineering simulations. We have identified four broad use cases of AI/ML applied to simulation: (1) Automatic parameter selection of simulation solvers to improve workflows and designer productivity (2) Augmenting simulation with AI/ML to accelerate simulation by factors of 100X (3) The use of AI/ML based generative design techniques to explore 10,000 designs automatically (4) Business intelligence to help improve engineering workflows. My talk will address three broad categories of AI/ML applied to simulation. (1) Top-down methods where we apply an AI/ML framework to a black box solver to train the ML models to improve run time (2) Bottom-up methods where we deeply embed the AI/ML methods inside the physics of our solvers. (3) Reduced order models where the order(?) of the solutions is reduced using AI/ML methods. We will illustrate each of these approaches on existing, commerical tools. As an example of a bottom-up approach, we will describe an ML-based Partial Differential Equation solver and apply it to accelerate Fluid Dynamics problems and will report our results on our Fluent CFD software. As an example of a top-down method, we will report on an ML framework to improve the productivity of any ML developer working in simulation. As an example of a reduced order model we will report on a hybrid digital twin tool called the Twin Builder. We will report on an end-to-end chip packaging solution using a combination of data-driven and physics-informed neural networks, as integrated within Ansys Redhawk/IcePak/Mechanical solutions for Conjugate Heat Transfer. We will describe approaches to support fast design exploration/optimization using ML frameworks. We will describe ML-enabled assistance in various steps of simulation workflows such as initial meshing, smart sub-modeling, user experience and automatic selection of parameters. We will report on automatically setting the best parameters in Fluent/Live AMG solver.

ABOUT: Prith Banerjee is the Chief Technology Officer of Ansys where he is responsible for leading the evolution of Ansys’ Technology strategy and champion the company’s next phase of innovation and growth Formerly, he was Executive Vice President, Chief Technology Officer of Schneider Electric. Previously, Prith was Managing Director of Global Technology Research and Development at Accenture. Formerly, he was Chief Technology Officer and Executive Vice President of ABB. Earlier, he was Senior Vice President of Research at HP and Director of HP Labs. Formerly, Prith was Dean of the College of Engineering at the University of Illinois at Chicago. Formerly, he was the Walter P. Murphy Professor and Chairman of Electrical and Computer Engineering at Northwestern University. Prior to that, Prith was Professor of Electrical and Computer Engineering at the University of Illinois at Urbana-Champaign. In 2000, he founded AccelChip, a developer of products for electronic design automation, which was acquired by Xilinx Inc. in 2006. During 2005-2011, he was Founder, Chairman and Chief Scientist of BINACHIP Inc., a developer of products in electronic design automation. FastCompany listed Prith in their 100 top business leaders in 2009. He is a Fellow of the AAAS, ACM and IEEE, and a recipient of the 1996 ASEE Terman Award, the 2001 Taylor Booth Award from IEEE, and the 1987 NSF Presidential Young Investigator Award. Prith earned a B.Tech. in electronics engineering from the Indian Institute of Technology, Kharagpur, and an M.S. and Ph.D. in electrical engineering from the University of Illinois, Urbana.

Advancing Precision Medicine through Generative AI-driven Drug Development

Wednesday, July 12

Generative AI has the potential to revolutionize drug discovery and precision medicine, opening up a world of new possibilities for researchers and clinicians. By harnessing the power of deep learning algorithms, generative AI has the potential to identify promising candidates for new drugs as well as predict patient response to different treatments, allowing for more personalized and precise medicine. This talk will explore the ways in which generative AI is transforming drug discovery and precision medicine, and the potential it holds for future innovation in these fields.

ABOUT: Lip-Bu Tan has served as Executive Chair of the Board of Directors of Cadence since December 2021 and has been a member of the Cadence Board of Directors since February 2004. He served as CEO of the company from 2009 to 2021 and as President from 2009 to 2017. He also serves as chairman of Walden International, the venture capital firm he founded in 1987, and is a founding managing partner of Walden Catalyst Ventures. Prior to joining Cadence, Mr. Tan was Vice President at Chappell & Co. and held management positions at EDS Nuclear and ECHO Energy. Mr. Tan is a member of The Business Council and serves on the boards of directors of Intel Corporation, Schneider Electric SE, Credo Technology Group Holding Ltd., and Green Hills Software. He also serves on the Board of Trustees and the School of Engineering Dean’s Council at Carnegie Mellon University and on the University of California, Berkeley’s Engineering Advisory Board. Mr. Tan received the Semiconductor Industry Association (SIA)’s 2022 Robert N. Noyce Award and the Global Semiconductor Alliance (GSA)’s 2016 Morris Chang Exemplary Leadership Award. Mr. Tan received a BS from Nanyang University in Singapore, an MS in nuclear engineering from the Massachusetts Institute of Technology, and an MBA from the University of San Francisco.

AI Hardware Reliability and Safety Challenges to Enable the Future Metaverse

Thursday, July 13

The Metaverse can be described as a new layer bridging the gap between the digital world and the real one. Todays’ we are assisting to the development of a significant variety of tools enabling a very accurate digital emulation of the real world (e.g., for product design), or an augmented reality experience (e.g., for education purposes), or the creation of a coupled digital-twin of a real entity (e.g., for product manufacturing and maintenance), etc. The Future Metaverse should include, interconnect and enhance all these already existing capabilities into a unified universe, consisting of interoperable digital and real worlds, with inclusive accessibility to all. Artificial Intelligence (AI) will play a fundamental role in enabling all of this, by providing human interaction with the metaverse (e.g., through speech and gesture recognition, object detection in images/videos, tactile sensing, etc.) and allowing a personalized experience in the metaverse (e.g., based on individual preferences, possible disabilities, etc.).

Since AI will guide our integration in the future metaverse, reliability and safety of its implementing hardware with respect to faults and aging conditions possibly occurring during its operation in the field should be guaranteed. Safety and reliability challenges of the AI hardware to enable the future metaverse will be addressed.

ABOUT: Cecilia Metra is a Professor and the Deputy President of the School of Engineering at the University of Bologna, Italy, where she has worked since 1991, and from which she received the Laurea Degree in Electronic Engineering and the PhD in electronic engineering and computer science. In 2002, she was visiting faculty consultant for Intel Corporation. She is part of the Italian National Research Center on High Performance Computing, Big Data and Quantum Computing, and of the Italian Research Project on Security and Rights In the CyberSpace. She is 2022-2023 IEEE Director, Division V, and she was the 2019 President of the IEEE Computer Society. She is Co-Chair of the “IEEE Metaverse” Project of the IEEE Future Directions, and a member of several IEEE Committees, including the IEEE Conferences and the IEEE Award Committees. She was a member of the Board of Governors of the IEEE Computer Society and the IEEE CEDA. She was Editor-in-Chief of the IEEE Transactions on Emerging Topics in Computing, and Associate Editor-in-Chief of the IEEE Transactions on Computers. She contributed to numerous IEEE international conferences/symposia/workshops as General/Program Chair, Technical Program Committee member, and Keynote/Invited Speaker.