Published in Machine Learning for Cyber Physical Systems, 2021
Recommended citation: P Sharma, M Rüb, D Gaida, H Lutz, A Sikora. (2021). "Deep Learning in Resource and Data Constrained Edge Computing Systems." Machine Learning for Cyber Physical Systems. https://library.oapen.org/bitstream/handle/20.500.12657/46102/2021_Book_MachineLearningForCyberPhysica.pdf?sequence=1#page=47
Published in Part of special issue: 17th IFAC Conference on Programmable Devices and Embedded Systems PDES 2022 — Sarajevo, Bosnia and Herzegovina, 17-19 May 2022, 2022
Recommended citation: M Rüb, A Sikora. (2022). "A Practical View on Training Neural Networks in the Edge." 17th IFAC Conference on Programmable Devices and Embedded Systems PDES 2022. https://www.sciencedirect.com/science/article/pii/S2405896322003603#!
Published:
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Published:
Deep neural networks enable state-of-the-art accuracy in visual recognition tasks such as image classification and object recognition. However, modern networks contain millions of learned connections, and the current trend is toward deeper and more tightly connected architectures. This poses a challenge for the deployment of advanced networks on resource-constrained systems such as smartphones or mobile applications. To make neural networks on embedded devices more usable, there are different techniques to compress the models. In this Talk the most common compression algorithms will be presented and their functionality explained. Among the techniques presented will be pruning, quantization and others.
Undergraduate course, Furtwangen University, Mechanical and Medical Engineering, 2014
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Teaching SMEs, Hahn-Schickard, Artificial intelligence, 2014
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Lecture, Furtwangen University, Mechanical and Medical Engineering, 2015
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