Prof. David Atienza Alonso
Embedded Systems Lab., EPFL, Switzerland
David Atienza is Associate Professor of Electrical and Computer Engineering and leads the Embedded Systems Laboratory (ESL) at EPFL, Switzerland. He received his MSc and PhD degrees in Computer Science and Engineering from UCM (Spain) and IMEC (Belgium). His research interests focus on system-level design methodologies for energy-efficient edge multi-processor system-on-chip architectures (MPSoC), and next-generation smart embedded systems (particularly wearables), for the Internet of Things (IoT) era. In these fields, he is co-author of more than 250 publications, seven patents, and received several best paper awards in top conferences. He also was the Technical Program Chair of DATE 2015 and General Chair of DATE 2017. Dr. Atienza has received the DAC Under-40 Innovators Award in 2018, IEEE TCCPS Mid-Career Award in 2018, an ERC Consolidator Grant in 2016, the IEEE CEDA Early Career Award in 2013, the ACM SIGDA Outstanding New Faculty Award in 2012, and a Faculty Award from Sun Labs at Oracle in 2011. He is an IEEE Fellow and an ACM Distinguished Member.
Talk: Designing Intelligent Things for the Big Data Era
The Internet of Things (IoT) has been hailed as the next frontier of innovation in which our everyday objects are connected in ways that improve the quality of our living environments, as well as industrial efficiency. As a result, the IoT concept is poised to reach 70 billion connected “things” (devices) by 2025. However, major key challenges remain in achieving this potential due to the inherent resource-constrained nature of IoT systems, coupled with the computing power requirements of Big Data applications, which can result in degraded and unreliable behavior of IoT nodes, or a global energy crisis when IoT is fully deployed in the near future. In this keynote, first, the challenges of ultra-low power design and communication in IoT devices will be presented. Then, possible design approaches for wearables and next-generation intelligent things will be analyzed to successfully develop energy-efficient intelligent environments for the Big Data Era. These new design approaches for intelligent things combine new embedded multi-core architectures with neural network accelerators, as well as including novel nanotechnologies, to gracefully adapt the energy consumption and precision of the IoT application outputs according to the requirements of our surrounding living environment, as living organisms do to efficiently operate.
Dr. Abdel Labbi
IBM Research, Zurich, Switzerland
Abdel Labbi is an IBM Distinguished Engineer and Distinguished Research Staff Member (RSM), currently leading a global team of Engineers and Researchers building Enterprise scale AI Platforms. Since he joined IBM in 2000, he has led several teams and projects delivering Services & Software innovations that support both IBM internal business units and external clients in various industries. These innovations span the whole value chain of business, from Supply Chain Optimization to Sales & Marketing Effectiveness, Operations, Financial Planning, and Operational Risk Management. Prior to joining IBM, Dr. Labbi was Assistant Professor at the University of Geneva (Switzerland). He holds a PhD in applied Mathematics from the University of Grenoble (France), and an MBA from Henley Business School (UK).
Talk: Scalable AI Platforms for IoT Applications: Examples from Select Industries
Building scalable AI platforms is a significant endeavor that entails several challenges across the whole pipeline ranging from reliable data acquisition and transformation to AI models design, training and deployment in specific workflows/applications. A key challenge in such platforms is the trade-off between in-situ vs. remote data processing and AI models training. Recent developments both in Distributed Systems engineering and distributed AI models training make it possible to build reliable, yet high performance, IoT applications. We will show a few real examples in industries such as Transportation, Manufacturing, and Construction.
Prof. Keiichi Yasumoto
Graduate School of Science and Technology, NAIST, Japan
Keiichi Yasumoto is a Professor with the Graduate School of Science and Technology, Nara Institute of Science and Technology (NAIST). He has published a number of papers in mobile and ubiquitous computing areas, especially on participatory sensing systems, activity recognition in smart home, wireless sensor network and distributed edge computing platform. He served as a general co-chair of IEEE PerCom 2019 and participated in organization of many top-tier conferences including MobiQuitous, ICDCS, Pervasive, FORTE and MDM.
Talk: Toward Smarter Smarthome: Vision and Challenges
Talk abstract will be published soon...