A Design-Space Exploration Framework for Application-Specific Machine Learning Targeting Reconfigurable Computing
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-42921-7_27
Reference3 articles.
1. Blott, M., et al.: FINN-R: an end-to-end deep-learning framework for fast exploration of quantized neural networks. ACM Trans. Reconfig. Technol. Syst. 11(3) (2018). https://doi.org/10.1145/3242897
2. Mahmood, S., Scharoba, S., Schorlemer, J., Schulz, C., Hubner, M., Reichenbach, M.: Detecting improvised land-mines using deep neural networks on GPR image dataset targeting FPGAs. In: 2022 IEEE Nordic Circuits and Systems Conference, NORCAS 2022 - Proceedings (2022). https://doi.org/10.1109/NorCAS57515.2022.9934735
3. Schorlemer, J., Jebramcik, J., Baer, C., Rolfes, I., Schulz, C.: A statistical FDFD simulator for the generation of labeled training data sets in the context of humanitarian demining using GPR. In: 2022 IEEE MTT-S International Conference on Numerical Electromagnetic and Multiphysics Modeling and Optimization, NEMO 2022, pp. 1–3 (2022). https://doi.org/10.1109/NEMO51452.2022.10038521
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