Keynotes at DATE 2019

 Keynote 1    

 Keynote 2    

 Keynote 3    

 Keynote 4    

 Keynote 5    

Keynote 1 Working with Safe, Deterministic and Secure Intelligence from Cloud to Edge
Speaker Dr. Astrid Elbe
Managing Director Intel Labs Europe


The Internet of Things (IoT) will be the largest revolution in the data economy. At Intel, we understand the exponential power of data, and we’re making it practical and economical to put it to work from the edge to the cloud. Intel® technologies purpose-built for IoT deliver optimized performance at every point, practical ways to use artificial intelligence, broad connectivity support, and a built‐in foundation of functional safety, time determinism and security to help protect and make dependable your data and systems. By harnessing the massive flood of data generated by connected things—and using it to gain actionable insights—we’ll accelerate business transformation to a degree never seen before.
Managing services and infrastructure at the edge is a complex balancing act that has to meet much more demanding timing and dependability constraints and requires vastly more speed and precision than in a conventional cloud data center. Satisfying the competing objectives of stringent Quality of Service (QoS) and workload consolidation in this complex IoT environment requires new approaches and advancements. Virtualization alone does not deliver the full potential for this IoT transformation. E.g. for challenging industrial workloads an automatic and self-managing approach will be needed.


Dr. Astrid Elbe is Managing Director of Intel Labs Europe leading Intel’s research efforts in Europe as an essential group driving Intel strategy and hence company transformation.

The organization is focused on Edge Computing Research with a particular emphasis on Dependable Cyber Physical Systems.

Astrid brings >20 years of experience in semiconductor industry in various R&D and Engineering Management roles at Infineon Technologies Security and Wireless Business Group and within Intel Product Divisions. She studied Physics and Mathematics as well as Technology and Innovation Management. Astrid holds a PhD in Surface Physics and has more than 20 patents in areas including cryptography and microarchitecture.

Keynote 2 Assisted and Automated Driving
Speaker Prof. Dr.-Ing. Jürgen Bortolazzi
Director Driver Assistance Systems and Highly Automated Driving
Dr. Ing. h.c. F. Porsche AG


Since the introduction of Park Distance Control and Adaptive Cruise Control in the Mid 2000s, PORSCHE follows a systematic strategy to adapt driver assistance and automated driving to their product lines. There is no contradiction to the philosophy of a sports car: customers that enjoy driving on their own in case of appropriate traffic conditions expect significant ease of driving in stressful, time-consuming situations like traffic jams, or heavily occupied parking spaces. Furthermore, new functionalities like the predictive Innodrive system enabling efficient cruise control based on sophisticated planning algorithms provides a perfect contribution to the PORSCHE Intelligent Performance strategy.

Although the common discussion focuses on the higher levels of automation from SAE Level 3 to Level 4, at least for the next decade Level 1 and 2 systems will play a significant role being the technological state-of-the-art for a majority of cars. Therefore, PORSCHE focuses on increasing the performance and functionality of Level1/2 driver assistance system in parallel to participating in development programs to enable Level3/4 automated driving. This offers the opportunity to systematically build the necessary competency both in the technological fields of sensing, sensor fusion, planning and control as well as the necessary processes, methods and tools that are mandatory to develop, approve and release higher level automated systems. Systems Engineering has to be combined with approaches to process very large amounts of data whereas traditional random road-based testing has to be replaced by a combination of virtual and systematic real-world testing. Last but not least, a new end-to-end EE architecture is necessary to provide the seamless integration of the vehicle into an IT based service infrastructure.
The keynote will address the following topics:
Benefits and challenges of assisted and automated driving
Status of L1/2 assisted driving
Challenges and technology assets for L3/4 automated driving
Data driven development methodologies
End-to-End Electronic Architecture (E3)


Jürgen Bortolazzi serves as head of engineering for advanced driver assistance and automated driving at Porsche. During his 25-year industrial career, Mr. Bortolazzi had several leading positions at Porsche and Mercedes Benz Cars focusing on E/E Architecture, electronic safety and driver assistance systems as well as intelligent lighting. He has initiated and managed several vehicle industry wide activities such as the OSEK/VDX and AUTOSAR software architecture, the FlexRay vehicle communication system, model-based E/E Architecture Development as well as intelligent LED lighting systems.

Since 2007, Mr. Bortolazzi is a honorary professor at the Karlsruhe Institute of Technology and is engaged in teaching Systems Engineering for Automotive Electronics as well as graduating PhD students. Prior to his industrial career, Mr. Bortolazzi headed the Electronic Systems and Microsystems department at the Computer Science Research Institute in Karlsruhe and the Systems Engineering research group at the Fraunhofer Institute/University of Erlangen-Nuernberg. Mr. Bortolazzi received his Dipl.-Ing. in Electronic Engineering as well as his PhD degree from the University of Erlangen-Nuernberg.

Keynote 3 Leonardo da Vinci, Humanism and Engineering between Florence and Milan
Speaker Dr. Claudio Giorgione
Curator, Museo Nazionale della Scienza e della Tecnologia Leonardo da Vinci, Milano, Italy


The machines and mechanical elements drawn by Leonardo through the course of his itinerary as engineer and technologist belong to the most disparate fields, highlighting his curiosity about the technological culture of his times. Just as for the other sectors of his activity, the first machines depicted by Leonardo follow in the tradition of the Renaissance Florentine workshop and are characterized by a practical, empirical approach aimed at resolution of problems progressively as they arose. During his first Milanese period (1482-1499), Leonardo was experimenting with, and refining ever more effective graphical systems of representation, which he would proceed in applying also to other sectors, like anatomy, architecture, and military engineering. Sections, prospect views, and transparent views were used to decompose machines into their constituent elements, finding solutions for automating and rendering more efficient the existing traditional mechanisms, or for conceiving completely new mechanisms. Leonardo moved, particularly in the 1490s, from documentation of practical problems to a more theoretical analysis of the principles regulating the functioning of machines, from the study of mechanical elements to their inter-relation. The studies on friction and on motion in general are to be inserted into this perspective, which led him to the idea of compiling a treatise on mechanics, based on the analysis of mechanisms and gears, the so-called "elementi macchinali".


Claudio Giorgione graduated in History of Art at the Università degli Studi di Milano with a dissertation about the Renaissance milanese painter Bernardino Luini. He has been working at Museo Nazionale della Scienza e della Tecnologia Leonardo da Vinci since 1997, and is currently the Curator of the Leonardo Art and Science Department of the Museum. As an art historian he holds lectures and conferences. He is the author of the book "Leonardo da Vinci. The collection of models, "published by the Museum in 2009, and has curated and co-edited the exhibition and catalogue "Leonard de Vinci. La nature e l'invention" displayed at the Citè des Sciences in Paris, and published by Editions de la Martiniere in 2012 during for the exhibition at the Cité des Sciences in Paris "Leonard de Vinci. Projects, dessins, machines ". He also curated the exhibition "Leonardo da Vinci, nature, art and science" in Incheon, South Korea, in 2009, and the exhibition "The Ideal City in the Renaissance" in the Pavillion of National Museum of Shanghai during the World Expo 2010 in Shanghai, China. He also wrote the essays "Leonardo e il disegno di Macchine" in the volume "Leonardo da Vinci. 1452-1519", Skira, 2015, "Leonardo, la Fabbrica e il Tiburio" in the volume "Leonardo da Vinci and the construction of the Cathedral of Milan", Silvana Editoriale, 2012 and "Leonardo da Vinci and machines: body shape and automation" for the catalogue of the exhibition "Bodies, automata and robots", held in Lugano, Switzerland, 2009.

Keynote 4 Heterogeneous, High Scale Computing in the Era of Intelligent, Cloud-Connected Devices
Speaker David Pellerin
Amazon, US


Rapid advances in connected devices, coupled with machine learning and "data lake" methods of advanced analytics, have led to an explosion in demand for non-traditional, highly scalable computing and storage platforms. This increasing demand is being seen in the public cloud as well as in cloud-connected IoT edge devices. AI/ML is at the heart of many the newest, most advanced analytics and IoT applications, ranging from robotics and autonomous vehicles, to cloud-connected products such as Alexa, to smart factories and consumer-facing services in the financial and healthcare sectors. In support of these important workloads, alternative methods of computing are being deployed in the cloud and at the edge. These alternative, heterogeneous computing methods include CPUs, GPUs, FPGAs, and other emerging acceleration technologies. This talk presents examples of such use-cases within Amazon, as well examples of how Amazon customers increasingly rely on AI/ML, accelerated using alternative computing methods and coupled with smart, cloud-connected devices to create next-generation intelligent products. The talk will conclude with examples of how cloud-based semiconductor design is being enhanced using these same methods.


David Pellerin serves as Head of Worldwide Business Development for Infotech/Semiconductor at Amazon Web Services. Prior to joining AWS, Mr. Pellerin had a career in electronic design automation and hardware-accelerated reconfigurable computing. He has experience with digital logic simulation and optimization, high-level synthesis, grid and cluster computing, and embedded systems for image, video, and network processing. He has published five Prentice Hall technical books on EDA and FPGA-related topics.

Keynote 5 A Fundamental Look at Models and Intelligence
Speaker Prof. Edward A. Lee
University of California, Berkeley, US


Models are central to building confidence in complex software systems. Type systems, interface theories, formal semantics, concurrent models of computation, component models, and ontologies all augment classical software engineering techniques such as object-oriented design to catch errors and to make software more modular and composable. Every model lives within a modeling framework, ideally giving semantics to the model, and many modeling frameworks have been developed that enable rigorous analysis and proof of properties. But every such modeling framework is an imperfect mirror of reality. A computer system operating in the physical world may or may not accurately reflect behaviors predicted by a model, and the model may not reflect behaviors that are critical to correct operation of the software. Software in a cyber-physical system, for example, has timing properties that are rarely represented in formal models. As artificial intelligence gets more widely used, the problem gets worse, with predictability and explainability seemingly evaporating. In this talk, I will examine the limitations in the use of models. I will show that two very different classes of models are used in practice, classes that I call "scientific models" and "engineering models." These two classes have complementary properties, and many misuses of models stem from confusion about which class is being used. Scientific models of intelligent systems are very different from engineering models.


Prof. Edward A. Lee Lee is the Robert S. Pepper Distinguished Professor of the Graduate School in Electrical Engineering and Computer Science at UC Berkeley, where he has been on the faculty since 1986. He is the author of Plato and the Nerd - The Creative Partnership of Humans and Technology (MIT Press, Fall 2017), Introduction to Embedded Systems - A Cyber-Physical Systems Approach (MIT Press, 2017), a number of other textbooks and research monographs, and more than 300 papers and technical reports. Lee has delivered more than 180 keynote talks and other invited talks at venues worldwide and has graduated at least 35 PhD students. Professor Lee's research group studies cyber-physical systems, which integrate physical dynamics with software and networks. His focus is on the use of deterministic models as a central part of the engineering toolkit for such systems. He has led the development of several influential open-source software packages, notably Ptolemy and its various spinoffs.

Lee is the director of iCyPhy, the Berkeley Industrial Cyber-Physical Systems Research Center, and the Ptolemy project. From 2013-2017, he was director of the nine-university TerraSwarm Research Center. From 2005-2008, he served as chair of the EE Division and then chair of the EECS Department at UC Berkeley. He received his BS degree in 1979 from Yale University, with a double major in Computer Science and Engineering and Applied Science, an SM degree in EECS from MIT in 1981, and a PhD in EECS from UC Berkeley in 1986. From 1979 to 1982 he was a member of technical staff at Bell Labs in Holmdel, New Jersey, in the Advanced Data Communications Laboratory. He is a co-founder of BDTI, Inc., where he is currently a Senior Technical Advisor, and has consulted for a number of other companies.

Lee is a Fellow of the IEEE, was an NSF Presidential Young Investigator, won the 1997 Frederick Emmons Terman Award for Engineering Education, and received the 2016 Outstanding Technical Achievement and Leadership Award from the IEEE Technical Committee on Real-Time Systems (TCRTS).