Conveners
Resource-Aware ML: Research Area Meeting
- Sebastian Buschjäger (Lamarr Institute for ML and AI, TU Dortmund)
Description
This session will be split into two halves: In the first half, we present / discuss / review two contributions from Lamarr to resource-aware ML. In the second half, we will focus more on sustainability in computer science in general and how this might shape our research in resource-aware ML@Lamarr.
-
19/02/2025, 10:00
In this talk, we introduce Splitting Stump Forests – small ensembles of weak learners extracted from a trained random forest. The high memory consumption of random forest ensemble models renders them unfit for resource-constrained devices. We show empirically that we can significantly reduce the model size and inference time by selecting nodes that evenly split the arriving training data and...
Go to contribution page -
Sebastian Buschjäger (Lamarr Institute for ML and AI, TU Dortmund)19/02/2025, 10:30
As machine learning models become increasingly integrated into various applications, the need for resource-aware deployment strategies becomes paramount. One promising approach for optimizing resource consumption is rejection ensembles. Rejection ensembles combine a small model deployed to an edge device with a large model deployed in the cloud, with a rejector tasked to determine the most...
Go to contribution page -
19/02/2025, 11:00
Quadratic unconstrained binary optimization (QUBO) problems are well-studied, not least because they can be approached using contemporary quantum annealing or classical hardware acceleration. However, due to limited precision and hardware noise, the effective set of feasible parameter values is severely restricted. As a result, otherwise solvable problems become harder or even intractable. In...
Go to contribution page -
19/02/2025, 11:30
Can we give outdated gaming consoles a second life in research and teaching? With GENUSES, we upcycle every single component of old Playstation 4 consoles in order to let them serve as a cost-effective teaching kit.
There is a video, check it out: https://www.youtube.com/watch?v=9iUO86Y1t8w
Go to contribution page
Talk is done by Christian Hakert -
Sebastian Buschjäger (Lamarr Institute for ML and AI, TU Dortmund)19/02/2025, 12:00
A long, long time ago, in a far away land, some smart people thought about how to connect hardware and machine learning to make ML and hardware more resource-aware. At this time, the term "resource-aware ML" came along. While our roots go back some 10-15 years now, the term "resource-aware ML" only partially reflects the current trend in ML research. In fact, most new projects and ideas...
Go to contribution page