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FAT: Training Neural Networks for Reliable Inference Under Hardware Faults.

, , , , и . ITC, стр. 1-10. IEEE, (2020)

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Brigitte Kees

Brigitte Kees

Sabine Kees

Norbert Kees

Andreas Kees

 

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Using C-to-gates to program streaming image processing kernels efficiently on FPGAs., , и . FPL, стр. 626-630. IEEE, (2009)Streaming Systems in FPGAs., и . SAMOS, том 5114 из Lecture Notes in Computer Science, стр. 147-156. Springer, (2008)A Hash Table for Line-Rate Data Processing., , , и . ACM Trans. Reconfigurable Technol. Syst., 8 (2): 13:1-13:15 (2015)FINN: A Framework for Fast, Scalable Binarized Neural Network Inference., , , , , , и . CoRR, (2016)EcoFlow: Efficient Convolutional Dataflows for Low-Power Neural Network Accelerators., , , , , , , и . CoRR, (2022)Training Deep Neural Networks in Low-Precision with High Accuracy Using FPGAs., , , , и . FPT, стр. 1-9. IEEE, (2019)The Future of Flexible HW Platform Architectures Panel Discussion., , , , , и . DATE, стр. 634. IEEE Computer Society / ACM, (2000)Memory Efficient Design of an MPEG-4 Video Encoder for FPGAs., , , , и . FPL, стр. 391-396. IEEE, (2005)TriMedia CPU64 Architecture., , , , , , , , , и . ICCD, стр. 586-592. IEEE Computer Society, (1999)Benchmarking vision kernels and neural network inference accelerators on embedded platforms., , , , , , , , и . J. Syst. Archit., (2021)