Reference : Profiling the real world potential of neural network compression
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Engineering, computing & technology : Computer science
http://hdl.handle.net/10993/52409
Profiling the real world potential of neural network compression
English
Lorentz, Joe mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Hartmann, Thomas mailto [DataThings S.A.]
Moawad, Assaad mailto [DataThings S.A.]
Aouada, Djamila mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > CVI2 >]
1-Aug-2022
2022 IEEE International Conference on Omni-layer Intelligent Systems (COINS), Barcelona 1-3 August 2022
IEEE
Yes
978-1-6654-8356-8
2022 IEEE International Conference on Omni-layer Intelligent Systems (COINS)
from 01.08.2022 to 03.08.2022
[en] machine learning ; computer vision ; model compression
[en] Abstract—Many real world computer vision applications are required to run on hardware with limited computing power, often referred to as ”edge devices”. The state of the art in computer vision continues towards ever bigger and deeper neural networks with equally rising computational requirements. Model compression methods promise to substantially reduce the computation time and memory demands with little to no impact on the model robustness. However, evaluation of the compression is mostly based on theoretic speedups in terms of required floating-point operations. This work offers a tool to profile the actual speedup offered by several compression algorithms. Our results show a significant discrepancy between the theoretical and actual speedup on various hardware setups. Furthermore, we show the potential of model compressions and highlight the importance of selecting the right compression algorithm for a target task and hardware. The code to reproduce our experiments is available at https://hub.datathings.com/papers/2022-coins.
http://hdl.handle.net/10993/52409
10.1109/COINS54846.2022.9854973
The final authenticated version is available online at https://doi.ieeecomputersociety.org/10.1109/COINS54846.2022.9854973
FnR ; FNR14297122 > Joe Lorentz > DETECT > Towards Edge-optimized Deep Learning For Explainable Quality Control > 01/01/2020 > 31/12/2023 > 2019

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