@article{Dom2021,
Author = {Domenik Helms, Karl Amende, Saqib Bukhari, Thies de Graaff, Alexander Frickenstein, Frank Hafner, Tobias Hirscher, Sven Mantowsky, Georg Schneider, Manoj-Rohit Vemparala},
Title = {Optimizing Neural Networks for Embedded Hardware},

Journal = {Proceedings of the 2021 Conference on Synthesis, Modeling, Analysis and Simulation Methods and Applications to Circuit Design},
Year = {2021},
Pages = {1-6},
Url = {https://ieeexplore.ieee.org/document/9547911},
type = {article},
Abstract = {Neural networks are a pervasive technology, which
is, however, still held back in the area of embedded systems by
the high resource requirements, especially memory size, memory
access time and power dissipation. In recent years, several
different methods have been proposed to transform given neural
networks in such a way that they can get by with much fewer
resources while maintaining almost the same accuracy. This work
reviews, categorizes and describes the state of the art in adapting
and simplifying neural networks to make them better applicable
to embedded systems. Even though we developed this study from
a purely automotive context, the techniques described are also
valid in other areas.}
}
@COMMENT{Bibtex file generated on }