A Hybrid Performance Prediction Approach for Fully-Connected Artificial Neural Networks on Multi-core Platforms

Author:

Dariol Quentin,Le Nours Sebastien,Pillement Sebastien,Stemmer Ralf,Helms Domenik,Grüttner Kim

Publisher

Springer International Publishing

Reference19 articles.

1. Banbury, C., et al.: Micronets: neural network architectures for deploying tinyml applications on commodity microcontrollers. In: Proceedings of Machine Learning and Systems (2021)

2. Chen, Y.H., Emer, J., Sze, V.: Using dataflow to optimize energy efficiency of deep neural network accelerators. IEEE Micro (2017)

3. Galanis, I., Anagnostopoulos, I., Nguyen, C., Bares, G., Burkard, D.: Inference and energy efficient design of deep neural networks for embedded devices. In: IEEE Computer Society Annual Symposium on VLSI (ISVLSI) (2020)

4. Garbay, T., et al.: CNN inference costs estimation on microcontrollers: the est primitive-based model. In: 2021 28th IEEE International Conference on Electronics, Circuits, and Systems (ICECS) (2021)

5. Hasan, M.: State of iot 2022: Number of connected iot devices growing 18% to 14.4 billion globally, May 2022. https://iot-analytics.com/number-connected-iot-devices/, accessed: 07.06.2022

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