High-Level Online Power Monitoring of FPGA IP Based on Machine Learning
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-29970-4_9
Reference13 articles.
1. Richa, M., Prévotet, J.-C., Dardaillon, M., Mroué, M., Samhat, A.E.: High-level early power estimation of FPGA IP based on machine learning. In: 29th IEEE International Conference on Electronics, Circuits, and Systems (ICECS), pp. 1–4 (2022). https://doi.org/10.1109/ICECS202256217.2022.9970952
2. Richa, M., Prévotet, J.-C., Dardaillon, M., Mroué, M., Samhat, A.E.: An automated and centralized data generation and acquisition system. In: 28th IEEE International Conference on Electronics, Circuits, and Systems (ICECS), pp. 1–4 (2021). https://doi.org/10.1109/ICECS53924.2021.9665490. ISBN 9781728182810
3. Akgun, G., Ali, M., Gohringer, D.: Power-aware computing systems on FPGAs: a survey. In: 31st International Conference on Field-Programmable Logic and Applications (FPL), pp. 45–51 (2021). https://doi.org/10.1109/FPL53798.2021.00016
4. Davis, J.J., Hung, E., Levine, J.M., Stott, E.A., Cheung, P.Y., Constantinides, G.A.: KAPow: high-accuracy, low-overhead online per-module power estimation for FPGA designs. ACM Trans. Reconfigurable Technol. Syst. (2018). https://doi.org/10.1145/3129789
5. Gallo, C.: Artificial neural networks. In: Convolutional Neural Networks in Visual Computing, pp. 1–426 (2011). https://doi.org/10.4324/9781315154282-3
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