Enabling ultra-low Power Machine Learning at the Edge
June 7-10, 2021 | Online
tinyML events are going “global”, virtually. After having to postpone a live event that was to be held in July 2021 in Cyprus, we will be going online the week of June 7, 2021. Even though it is virtual it will still be a “European focused” event, with speakers and participants showcasing technology from the EMEA region.
tinyML is a fast growing branch of machine learning technologies, architectures and approaches dealing with machine intelligence at the very edge. It is broadly defined as integrated, “full-stack” (HW-SYS-SW-apps), ML architectures, techniques, tools and approaches capable of performing on-device analytics for a variety of sensing modalities (vision, audio, motion, environmental, human health monitoring etc.) at extreme energy efficiency, typically in the single mW (and below) power range, enabling machine intelligence right at the boundary of the physical and digital worlds.
Call for Presentations to come
University of Cyprus
ETH-Zurich and Universita di Bologna, Italy
Qualcomm, the Netherlands
University of Cyprus
Arizona State University, USA
KU Leuven, Belgium
Emza visual sense, Isreal
Information to come
Registration to open soon
Luca Benini holds the chair of digital Circuits and systems at ETHZ and is Full Professor at the Universita di Bologna. He received a PhD from Stanford University. He has been visiting professor at Stanford University, IMEC, EPFL. In 2009-2012 he served as chief architect in STmicroelectronics France.
Dr. Benini's research interests are in energy-efficient parallel computing systems, smart sensing micro-systems and machine learning hardware. He has published more than 1000 peer-reviewed papers and five books. He is an ERC-advanced grant winner, a Fellow of the IEEE, of the ACM and a member of the Academia Europaea. He is the recipient of the 2016 IEEE CAS Mac Van Valkenburg award and of the 2019 IEEE TCAD Donald O. Pederson Best Paper Award.
Tijmen Blankevoort is the team lead for neural network efficiency research in Qualcomm. With a background in Japanese studies, Mathematics and Artificial Intelligence; he founded deep-learning start-up Scyfer in 2013, together with Prof. Max Welling, which was acquired by Qualcomm in 2017. He is now conducting research in the areas of quantization, neural network compression, conditional computing and anything else related to running deep learning efficiently. In his spare time, Tijmen loves to play Magic: The Gathering, and is a fervent molecular gastronomy cook.
Peter Debacker received the M.Sc. (Hons.) degree in electrical engineering from the Katholieke Universiteit Leuven, Leuven, Belgium, in 2004. He worked with Philips as a system engineer and at Essensium as a System Architect before joining IMEC, Leuven, in 2011. He is currently Program Manager in the Semiconductor Technology and Systems division. He leads a team that researches semiconductor technology, architecture and algorithms to create efficient AI hardware ranging from DNN accelerators, to in-memory compute and neuromorphic hardware. Besides AI specific hardware, he works on power-performance-area (PPA) optimization of scaled CMOS technologies (for 3nm and beyond), emerging memories and beyond CMOS technologies. In his past he has worked on IMEC’s low-power digital chip and processor architectures and implementation in advanced technology nodes. His current research interests include AI, machine learning and neuromorphic computing, computer architectures, design methodologies, design-technology co-optimization, reliability, variability and low power design.
Tomas Edsö, Senior Principal Engineer, is currently acting as HW Tech Lead within the Arm machine learning group. Tomas joined Arm Sweden Lund in 2008 as one of the founding members of the video IP company Logipard, and has a wealth of knowledge within Video standards, digital signal compression and signal processing.
For the latter years, Tomas has transitioned into the machine learning group within Arm, where he has been part of designing as well as defining and scoping Arm NPU products.
Tomas holds a master’s degree in engineering physics from the University of Lund, with one additional year of scholarship studies in signal processing and artificial intelligence at the University of California, Irvine. Tomas currently holds 17 patents.
Evgeni Gousev is a Senior Director of Engineering in Qualcomm Research. He leads HW R&D org in the Silicon Valley Center and is also responsible for developing ultra-low power embedded computing platform, including always on machine vision AI technology. He has been with Qualcomm Technologies, Inc. since 2005 after joining from IBM T.J. Watson Research Center where he drove projects in the field of advanced silicon technologies. From 1993 to 1998, Dr. Gousev held academic professorship appointments with Rutgers University and Hiroshima University (1997). Evgeni holds a M.S. degree in Applied Physics and a Ph.D. in Solid-State Physics. He has co-edited 24 books and published 163 papers and is an inventor on more than 60 issued and filed patents.
Marios Polycarpou is a Professor of Electrical and Computer Engineering and the Director of the KIOS Research Center for Intelligent Systems and Networks at the University of Cyprus. He received undergraduate degrees in Computer Science and in Electrical Engineering, both from Rice University, USA in 1987, and the M.S. and Ph.D. degrees in Electrical Engineering from the University of Southern California, in 1989 and 1992 respectively. His teaching and research interests are in intelligent systems and networks, adaptive and cooperative control systems, computational intelligence, fault diagnosis and distributed agents. Dr. Polycarpou has published more than 350 articles in refereed journals, edited books and refereed conference proceedings, and co-authored 7 books. He is also the holder of 6 patents.
Prof. Polycarpou is the recipient of the 2016 IEEE Neural Networks Pioneer Award. He is a Fellow of IEEE and IFAC, and he received with his co-authors the 2014 Best Paper Award for the journal Building and Environment (Elsevier). Prof Polycarpou served as the President of the IEEE Computational Intelligence Society (2012-2013), as the Editor-in-Chief of the IEEE Transactions on Neural Networks and Learning Systems (2004-2010), and as the President of the European Control Association (EUCA) in 2018-2019. He has participated in more than 70 research projects/grants, funded by several agencies and industry in Europe and the United States, including the prestigious European Research Council (ERC) Advanced Grant and the EU Teaming project. Prof. Polycarpou is a founding member of the Cyprus Academy of Sciences, Letters, and Arts.
Andreas Spanias is Professor in the School of Electrical, Computer, and Energy Engineering at Arizona State University (ASU). He is also the director of the Sensor Signal and Information Processing (SenSIP) center and the founder of the SenSIP industry consortium (also an NSF I/UCRC site). His research interests are in the areas of adaptive signal processing, speech processing, machine learning and sensor systems. He and his student team developed the computer simulation software Java-DSP and its award-winning iPhone/iPad and Android versions. He is author of two textbooks: Audio Processing and Coding by Wiley and DSP; An Interactive Approach (2nd Ed.). He contributed to more than 300 papers, 7 monographs 9 full patents, 6 provisional patents and 10 patent pre-disclosures. He served as Associate Editor of the IEEE Transactions on Signal Processing and as General Co-chair of IEEE ICASSP-99. He also served as the IEEE Signal Processing Vice-President for Conferences. Andreas Spanias is co-recipient of the 2002 IEEE Donald G. Fink paper prize award and was elected Fellow of the IEEE in 2003. He served as Distinguished Lecturer for the IEEE Signal processing society in 2004. He is a series editor for the Morgan and Claypool lecture series on algorithms and software. He received recently the 2018 IEEE Phoenix Chapter award with citation: “For significant innovations and patents in signal processing for sensor systems.” He also received the 2018 IEEE Region 6 Educator Award (across 12 states) with citation: “For outstanding research and education contributions in signal processing.”
Theocharis (Theo) Theocharides is an Associate Professor in the Department of Electrical and Computer Engineering and the Research Director at the KIOS Research and Innovation Center of Excellence at the University of Cyprus. Theocharis received his Ph.D. in Computer Engineering from Penn State University, working in the areas of low-power computer architectures and reliable system design with emphasis on computer vision and computational intelligence applications. Theocharis was honored with the Robert M. Owens Memorial Scholarship in May 2005. He has been with the Electrical and Computer Engineering department at the University of Cyprus since 2006, where he directs the Embedded and Application-Specific Systems-on-Chip Laboratory and with the KIOS CoE since its inception in 2008. His research focuses on the design, development, implementation and deployment of low-power and reliable on-chip application-specific architectures, low-power VLSI design, real-time embedded systems design and exploration of energy-reliability trade-offs for Systems on Chip and Embedded Systems. His focus lies on acceleration of computer vision and artificial intelligence algorithms in hardware, geared towards edge computing, and in designing self-aware, evolvable edge computing systems. He serves on several organizing and technical program committees of various conferences and is currently serving as the Application Track Chair for DATE. Theocharis is a Senior Member of the IEEE, a member of the ACM, and an Associate Editor for IEEE Consumer Electronics magazine, ACM Transactions on Embedded Computing Systems, and the ETRI journal. He also serves on the Editorial Boards of IEEE Design & Test magazine.
Marian Verhelst is an associate professor at the MICAS laboratories (MICro-electronics And Sensors) of the Electrical Engineering Department of KU Leuven. Her research focuses on embedded machine learning, hardware accelerators, self-adaptive circuits and systems, and low-power embedded sensing and processing. She received a PhD from KU Leuven in 2008, was a visiting scholar at the Berkeley Wireless Research Center (BWRC) of UC Berkeley in 2005, and worked as a research scientist at Intel Labs, Hillsboro OR, from 2008 till 2011.
Marian is a member of the DATE conference executive committee and was a member of the ESSCIRC and ISSCC TPCs and of the ISSCC executive committee. Marian is an SSCS Distinguished Lecturer, was a member of the Young Academy of Belgium, an associate editor for TCAS-II and JSSC and a member of the STEM advisory committee to the Flemish Government. Marian currently holds a prestigious ERC Starting Grant from the European Union.
Bio to come
Carlo Reita is the Director of Strategic Partnerships and Planning at CEA-Leti in Grenoble, France, where he oversees partnerships with major RTOs, EU and European PAs and the organization’s contact point for nanoelectronics R&D.