Reliable Robotics in Academia and Industry
Speaker: Daniel J. Sorin
Daniel J. Sorin is Professor of Electrical and Computer Engineering and of Computer Science at Duke University, where he has been on the faculty since 2002. He received a PhD and MS in electrical and computer engineering from the University of Wisconsin, and he received a BSE in electrical engineering from Duke University. He is the recipient of an NSF Career Award and the Imhoff Distinguished Teaching Award at Duke. He was a Visiting Fellow of the Royal Academy of Engineering (UK). His research interests are in computer architecture, with a focus on fault tolerance, verification, and memory system design. He is the author of “Fault Tolerant Computer Architecture” and a co-author of “A Primer on Memory Consistency and Cache Coherence.” He is the editor-in-chief of IEEE Computer Architecture Letters. He is a co-founder and Chief Architect at Realtime Robotics, Inc.
Robotics is an exciting field with many opportunities for innovation in hardware and software. To the uninitiated computer architect, including the speaker as of about 2014, robotics looks like any other domain for custom-purpose acceleration. Unfortunately for the field of robotics, robotics also presents a wide range of reliability challenges. Before one can demonstrate a new idea in robotics, one must often solve a slew of reliability problems. In this talk, I will first discuss the various tasks that robots perform, and then I will present the reliability challenges that must be overcome for robots to perform these tasks. Throughout, I will highlight the differences between my robotics experiences in academia and at a robotics startup company.
Memory-Centric Artificial Intelligence
Speaker: Damien Querlioz
Damien Querlioz is a CNRS Researcher at the Centre de Nanosciences et de Nanotechnologies of Université Paris-Saclay. His research focuses on novel usages of emerging non-volatile memory and other nanodevices, in particular relying on inspirations from biology and machine learning. He received his predoctoral education at Ecole Normale Supérieure, Paris and his PhD from Université Paris-Sud in 2009. Before his appointment at CNRS, he was a Postdoctoral Scholar at Stanford University and at the Commissariat a l'Energie Atomique. Damien Querlioz is the coordinator of the interdisciplinary INTEGNANO research group, with colleagues working on all aspects of nanodevice physics and technology, from materials to systems. He is a member of the bureau of the French Biocomp research network. He has co-authored one book, nine book chapters, more than 100 journal articles, and conference proceedings, and given more than 50 invited talks at national and international workshops and conferences. In 2016, he was the recipient of an ERC Starting Grant to develop the concept of natively intelligent memory. In 2017, he received the CNRS Bronze medal. He has also been a co-recipient of the 2017 IEEE Guillemin-Cauer Best Paper Award and of the 2018 IEEE Biomedical Circuits and Systems Best Paper Award.
When performing artificial intelligence tasks, central and graphics processing units consume considerably more energy for moving data between logic and memory units than for doing actual arithmetic. Brains, by contrast, achieve superior energy efficiency by fusing logic and memory entirely, performing a form of “in-memory” computing. Currently emerging memory nanodevices such as (mem)resistive, phase change, and magnetic memories give us an opportunity to achieve similar tight integration between logic and memory. In this talk, we will look at neuroscience inspiration to extract lessons on the design of in-memory computing systems. We will first study the reliance of brains on approximate memory Continue reading strategies, which can be reproduced for artificial intelligence. We will give the example of a hardware binarized neural network relying on resistive memory. Binarized neural networks are a class of deep neural networks discovered in 2016, which can achieve state-of-the-art performance with a highly reduced memory and logic footprint with regards to conventional artificial intelligence approaches. Based on measurements on a hybrid CMOS and resistive hafnium oxide memory chip exploiting a differential approach, we will see that such systems can exploit the properties of emerging memories without the need for error-correcting codes, and achieve extremely high energy efficiency. Then, we will present a second approach where the probabilistic nature of emerging memories, instead of being mitigated, can be fully exploited to implement a type of probabilistic learning. We show that the inherent variability in hafnium oxide memristors can naturally implement the sampling step in the Metropolis-Hastings Markov Chain Monte Carlo algorithm, and train experimentally an array of 16,384 memristors to recognize images of cancerous tissues using this technique. These results highlight the interest in understanding and embracing the unreliable nature of emerging devices in artificial intelligence designs.
European Space Agency and COTS
Speaker: Ali Zadeh
Ali Zadeh received his MSc in physics from University of Bergen (Norway) and PhD in physics at Brunel University West London (UK). In 1996 he joined the European Southern Observatory (Munich, Germany) as a CCD specialist. From end-1997 he worked at the European Space Agency (ESTEC, The Netherlands) as a radiation Effects Engineer (until 2007), the Head of Radiation Effects and Component Analysis Techniques Section (until end-2017) and Head of the Data Systems, Microelectronics and Component Technology Division (to present). He has, amongst others, been active in EEE Component related standardisation activities, held official conference functions and authored / co-authored numerous papers mainly in the EEE Component radiation effects field.
European Space Agency (ESA) develops state-of-the-art spacecraft addressing research and technology, with a view to their use for scientific purposes and for operational space application systems. Electrical, Electronic and Electro-mechanical (EEE) Components are crucial elements of space systems with a necessity to maintain required performances in their targeted application, for the duration of intended operation and in the harsh space environment. Thus, EEE Components developed for the Terrestrial market are typically unsuitable for space applications. Specific development/manufacturing methodologies according to pertinent standards are used to provide suitable components for space applications. Space Qualified components are therefore Continue reading predominantly used in ESA missions. To a limited extent and for performance reasons, ESA for a long time uses Commercial-Off-The-Shelf (COTS) EEE Components (e.g. FLASH memory devices). However, the advent of new-space and components from the automotive industry inventory opens potential solutions for certain space applications. This talk sheds light on ESA COTS approach by addressing several related topics.