Speaker
Dr
Sebastian Buschjäger
(Lamarr Institute, TU Dortmund University)
Description
The scientific area of Resource-Aware Machine Learning tries to "make the most out of a bad situation," i.e., match existing solutions' performance while only using a fraction of the resources or surpassing existing solutions' performance. To do so, we try to bridge the gap between the mathematical concepts of Machine Learning, their expression in software, and their execution in hardware. We study new algorithms for training small ML models, their post-processing (e.g., pruning) for model deployment, and their compilation to existing or new hardware.
