Dagstuhl-Seminar 24311
Resource-Efficient Machine Learning
( 28. Jul – 02. Aug, 2024 )
(zum Vergrößern in der Bildmitte klicken)
Permalink
Bitte benutzen Sie folgende Kurz-Url zum Verlinken dieser Seite:
https://www.dagstuhl.de/24311
Organisatoren
- Oana Balmau (McGill University - Montréal, CA)
- Matthias Böhm (TU Berlin, DE)
- Ana Klimovic (ETH Zürich, CH)
- Peter R. Pietzuch (Imperial College London, GB)
- Pinar Tözün (IT University of Copenhagen, DK)
Kontakt
- Andreas Dolzmann (für wissenschaftliche Fragen)
- Christina Schwarz (für administrative Fragen)
Gemeinsame Dokumente
- Dagstuhl Materials Page (Use personal credentials as created in DOOR to log in)
Programm
The advances in Machine Learning (ML) are mainly thanks to the exponential evolution of hardware, the availability of the large datasets, and the emergence of machine learning frameworks, which hide the complexities of the underlying hardware, boosting the productivity of data scientists. On the other hand, the computational need of the powerful ML models has increased five orders of magnitude in the past decade. This makes the current rate of increase in model parameters, datasets, and compute budget unsustainable. To achieve more sustainable progress in ML in the future, it is essential to invest in more resource-/energy-/cost-efficient solutions. In this Dagstuhl Seminar, we will explore how to improve ML resource efficiency through software/hardware co-design. We plan to take a holistic view of the ML landscape, which includes data preparation and loading, continual retraining of models in dynamic data environments, compiling ML on specialized hardware accelerators, and serving models for real-time applications with low-latency requirements and constrained resource environments.
This seminar aims at reasoning critically about how we build software and hardware for end-to-end machine learning. We hope that the discussions will lead to increased awareness for understanding the utilization of modern hardware and kickstart future developments to minimize hardware underutilization. We thus would like to bring together academics and industry across fields of data management, machine learning, systems, and computer architecture covering expertise of algorithmic optimizations in machine learning, job scheduling and resource management in distributed computing, parallel computing, and data management and processing. The outcome of the discussions in the seminar will therefore also positively impact the research groups and companies that rely on machine learning.
We have identified the following topics to be discussed during the seminar:
- Characterization and benchmarking of modern ML techniques
- Efficient scheduling of ML tasks
- Measuring sustainability
- Hardware-software co-design for ML
- Data pre-processing and loading
Creative Commons BY 4.0
Oana Balmau, Matthias Böhm, Ana Klimovic, Peter R. Pietzuch, and Pinar Tözün
Please log in to DOOR to see more details.
- Matthias Böhm (TU Berlin, DE) [dblp]
- Maximilian Böther (ETH Zürich, CH) [dblp]
- Marco Canini (KAUST - Thuwal, SA) [dblp]
- Jerónimo Castrillón-Mazo (TU Dresden, DE) [dblp]
- Patrick Damme (TU Berlin, DE) [dblp]
- Khuzaima Daudjee (University of Waterloo, CA) [dblp]
- Pamela Delgado (HES-SO Valais-Wallis - Siders, CH) [dblp]
- Thaleia Dimitra Doudali (IMDEA Software Institute - Madrid, ES) [dblp]
- Jens Hagemeyer (Universität Bielefeld, DE) [dblp]
- Steven Hand (Google - Mountain View, US) [dblp]
- Dagmar Kainmüller (Max-Delbrück-Centrum - Berlin, DE) [dblp]
- Fredrik Kjolstad (Stanford University, US) [dblp]
- Ana Klimovic (ETH Zürich, CH) [dblp]
- James Kwok (HKUST - Hong Kong, HK) [dblp]
- Manisha Luthra Agnihotri (TU Darmstadt, DE & DFKI - Darmstadt, DE) [dblp]
- Peter R. Pietzuch (Imperial College London, GB) [dblp]
- Tilmann Rabl (Hasso-Plattner-Institut, Universität Potsdam, DE) [dblp]
- Theo Rekatsinas (Apple - Zürich, CH) [dblp]
- Ties Robroek (IT University of Copenhagen, DK) [dblp]
- Stefanie Scherzinger (Universität Passau, DE) [dblp]
- Tom St. John (Meta - Menlo Park, US) [dblp]
- Foteini Strati (ETH Zürich, CH) [dblp]
- Shinya Takamaeda-Yamazaki (The University of Tokyo, JP) [dblp]
- Pinar Tözün (IT University of Copenhagen, DK) [dblp]
- Lluís Vilanova (Imperial College London, GB) [dblp]
- Eiko Yoneki (University of Cambridge, GB) [dblp]
- Cliff Young (Google - Mountain View, US) [dblp]
- Ehsan Yousefzadeh-Asl-Miandoab (IT University of Copenhagen, DK) [dblp]
Klassifikation
- Hardware Architecture
- Machine Learning
- Operating Systems
Schlagworte
- resource-efficient systems
- systems for machine learning
