Track B: Sustainable AI
Digital Europe and the Green Deal – thinking together the two megatrends of digitalization and sustainability is key. AI offers tremendous opportunities to help our society interact with nature in a sustainable way. At the same time, the environmental impact of AI itself cannot be ignored. The Sustainable AI track focuses on the measurement of that environmental impact and on the development of resource-efficient AI technologies. It covers all levels of AI technology development, from AI algorithms and programming frameworks down to hardware and compilation.
Timetable – Speaker
Mon, August 29, 2022
Opening Speech – 13:15-14:15
Course 1 – 14:30-17:15Silviu-Ioan Filip (Inria)
Silviu Filip is an Inria researcher working in Rennes, France. He received his PhD in Computer Science from ENS Lyon in 2016 working on efficient and scalable algorithms for digital filter design and was a postdoctoral researcher at the Mathematical Institute in Oxford during 2017 working on numerical algorithms for rational approximations of functions. His research interests are centred around number format optimization problems stemming from various application fields such as scientific computing, digital signal processing and more recently, deep learning.
Tools for DNN quantization
Tue, August 30, 2022
Keynote 1 – 09:00-10:00Michael Luck (King's College London)
Michael Luck is Professor of Computer Science in the Department of Informatics at King’s College London, where he also works in the Distributed Artificial Intelligence group, undertaking research into agent technologies and artificial intelligence. He is currently Director of the UKRI Centre for Doctoral Training in Safe and Trusted Artificial Intelligence, Director of the King’s Institute for Artificial Intelligence, and scientific advisor for Aerogility, ContactEngine and CtheSigns. He is co Editor-in-Chief of the journal, Autonomous Agents, and Multi-Agent Systems, and is a Fellow of the European Association for Artificial Intelligence and of the British Computer Society.
Artificial Intelligence: Towards safety and trust
|Course 2 – 10:30-13:15
Olivier Sentieys (University of Rennes & Inria)
Olivier Sentieys is a Professor at the University of Rennes holding the Inria Research Chair on Energy-Efficient Computing Systems. He is leading the Taran team common to Inria and IRISA Laboratory. His research interests are in the area of computer architectures, computer arithmetic, embedded systems and signal processing, with a focus on system-level design, energy-efficient hardware accelerators, approximate computing, fault tolerance, and energy harvesting sensor networks.
Hardware accelerators for DNNs
|Course 3 – 15:45-18:30
Christoph Lüth (DFKI Bremen)
Christoph Lüth is vice director of the research department Cyber-Physical Systems group at the German Research Centre for Artificial Intelligence (DFKI) in Bremen, and professor for computer science at the University of Bremen. His research covers the whole area of formal methods, from theoretical foundations to tool development and applications in practical areas such as robotics. He has authored or co-authored over eighty peer-reviewed papers and was the principal investigator in several successful research projects in this area.
An Introduction to the RISC-V ISA
Wed, August 31, 2022
Keynote 2 – 09:00-10:00Sophie Quinton (Inria)
Sophie Quinton is a research scientist in computer science at INRIA in Grenoble, France. Her research background is on formal methods for the design and verification of embedded systems, with an emphasis on real-time aspects. She is now studying the environmental impact of ICT, in particular on claims about the benefits of using digital technologies for GHG emissions mitigation.
ICT and sustainability
|Course 4 – 10:30-13:15
Richard Membarth (DFKI Saarbrücken & Technische Hochschule Ingolstadt)
Richard Membarth is a professor for system on a chip and AI for edge computing at the Technische Hochschule Ingolstadt (THI). He is also an affiliated professor at DFKI in Saarbrücken. Richard received the diploma degree in Computer Science from the Friedrich-Alexander University Erlangen-Nürnberg (FAU) and the postgraduate diploma in Computer and Information Sciences from the Auckland University of Technologies (AUT). In 2013, he received the PhD (Dr.-Ing.) degree from FAU on automatic code generation for GPU accelerators from a domain-specific language for medical imaging. After the PhD, he joined the Graphics Chair and the Intel Visual Computing Institute (IVCI) at Saarland University as a postdoctoral researcher. At the German Research Center for Artificial Intelligence (DFKI), he was a senior researcher and team leader for compiler technologies and high-performance computing. His research interests include parallel computer architectures and programming models with a focus on automatic code generation for a variety of architectures ranging from embedded systems to HPC installations for applications from image processing, computer graphics, scientific computing, and deep learning.
Code optimization via specialization
|Course 5 – 15:45-18:30
Anne-Laure Ligozat (ENSIIE & LISN)
Anne-Laure Ligozat is an associate professor in computer science at ENSIIE and LISN in Paris-Saclay, France. Her research interests are the environmental impacts of Information and Communication Technologies and in particular of Artificial Intelligence.
Carbon footprint of AI
Thu, Sep 01, 2022
Keynote 3 – 09:00-10:00Marcus Voß (Birds on Mars)
Marcus Voß is an AI Expert and Intelligence Architect at Birds on Mars, where he works on AI applications for sustainable use cases. He is an external lecturer on AI and data science at TU Berlin and CODE University of Applied Sciences. Previously, he was a Research Associate at TU Berlin, where he led the research group “Smart Energy Systems” at the DAI Lab. He is active as Community Lead for the building and transportation sector at Climate Change AI, an international initiative that brings together stakeholders around AI and climate change.
Applying Artificial Intelligence for climate action
|Course 6 – 10:30-13:15
Danilo Carastan dos Santos (Inria)
Danilo Carastan-Santos is a Post-doctoral researcher at the Laboratoire d’Informatique de Grenoble, France. Danilo received his PhD in 2019, in a double-degree between University Grenoble-Alpes, France, and the Federal University of ABC, Brazil. His thesis’ subject is on learning heuristics for resource management of High-Performance Computing (HPC) platforms. Danilo was a Post-doctoral researcher at the Federal University of Rio Grande do Sul, Brazil, in the subject of performance analysis and optimization of geophysics HPC applications. His research interests are in HPC resource management, parallel and distributed computing, sustainable computing, and Artificial Intelligence.
Measuring the energy consumption of AI
|Course 7 – 15:45-18:30
Titouan Vayer (ENS Lyon)
Titouan Vayer is currently a postdoctoral researcher at ENS Lyon and works on compressive learning problems. He worked during his thesis, which was obtained in 2020 in IRISA, Vannes, on optimal transport methods for machine learning: In particular in the context of graphs and heterogeneous data. Titouan Vayer is particularly interested in the theory of learning in complex settings where the data are large, structured and do not admit the same representation.
Less is more? How compressive learning and sketching for large-scale machine learning works
Fri, Sep 02, 2022
Course 8 – 09:00-12:00Dr. Pedro Porto Buarque de Gusmão (Cambridge Machine Learning Systems Lab)
Dr Pedro Porto Buarque de Gusmao is a Senior Research Associate in the Machine Learning Systems Lab at the University of Cambridge, where he coordinates the group’s research in Federated Learning. Pedro obtained his PhD degree from the Politecnico di Torino and worked as a Post-doctoral Research Assistant at the University of Oxford, developing robust indoor navigation systems for firefighters. His research interests lie in the intersection between Computer Vision, Machine Learning, and Distributed Systems. Besides his research role, Pedro is also one of the core developers of the Flower Federated Learning framework.
An introduction to federated learning with Flower Collaborative Wrap-Up – 12:15-13:15
Collaborative Wrap-Up – 12:15-13:15