Efficient Deployment of Neural Networks for Thermal Monitoring on AURIX TC3xx Microcontrollers

Heidorn C, Hannig F, Riedelbauch D, Strohmeyer C, Teich J (2024)


Publication Language: English

Publication Type: Conference contribution, Conference Contribution

Publication year: 2024

Event location: Angers FR

ISBN: 978-989-758-703-0

DOI: 10.5220/0000186800003702

Abstract

This paper proposes an approach for efficiently deploying neural network (NN) models on highly resource-constrained microcontroller architectures, particularly AURIX TC3xx microcontrollers. Here, compression and optimization techniques of the NN model are required to reduce execution time while maintaining accuracy on the target microcontroller. Furthermore, especially on AURIX TriCores that are frequently used in the automotive domain, there is a lack of support for automatic conversion and deployment of pretrained NN models. In this work, we present an approach that fills this gap, enabling the conversion and deployment of so-called thermal neural networks on AURIX TC3xx microcontrollers for the first time. Experimental results on three different NN types show that, when pruning of convolutional neural networks is applied, we can achieve a speedup of 2.7× compared to state-of-the-art thermal neural networks.

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How to cite

APA:

Heidorn, C., Hannig, F., Riedelbauch, D., Strohmeyer, C., & Teich, J. (2024). Efficient Deployment of Neural Networks for Thermal Monitoring on AURIX TC3xx Microcontrollers. In SCITEPRESS (Eds.), Proceedings of the 10th International Conference on Vehicle Technology and Intelligent Transport Systems (VEHITS). Angers, FR.

MLA:

Heidorn, Christian, et al. "Efficient Deployment of Neural Networks for Thermal Monitoring on AURIX TC3xx Microcontrollers." Proceedings of the 10th International Conference on Vehicle Technology and Intelligent Transport Systems (VEHITS), Angers Ed. SCITEPRESS, 2024.

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