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FINN-R: An End-to-End Deep-Learning Framework for Fast Exploration of Quantized Neural Networks., , , , , , , and . ACM Trans. Reconfigurable Technol. Syst., 11 (3): 16:1-16:23 (2018)SYQ: Learning Symmetric Quantization for Efficient Deep Neural Networks., , , and . CVPR, page 4300-4309. Computer Vision Foundation / IEEE Computer Society, (2018)Trainable Preprocessing for Reduced Precision Neural Networks., , , , and . EUSIPCO, page 1546-1550. IEEE, (2021)Evaluation of Optimized CNNs on Heterogeneous Accelerators Using a Novel Benchmarking Approach., , , , , , , , , and . IEEE Trans. Computers, 70 (10): 1654-1669 (2021)FAT: Training Neural Networks for Reliable Inference Under Hardware Faults., , , , and . ITC, page 1-10. IEEE, (2020)Memory-Efficient Dataflow Inference for Deep CNNs on FPGA., , , , , and . FPT, page 48-55. IEEE, (2020)FINN: A Framework for Fast, Scalable Binarized Neural Network Inference., , , , , , and . CoRR, (2016)Kernel Normalised Least Mean Squares with Delayed Model Adaptation., and . ACM Trans. Reconfigurable Technol. Syst., 13 (2): 7:1-7:30 (2020)Distributed kernel learning using Kernel Recursive Least Squares., , , and . ICASSP, page 5500-5504. IEEE, (2015)Accuracy to Throughput Trade-Offs for Reduced Precision Neural Networks on Reconfigurable Logic., , , , , , , and . ARC, volume 10824 of Lecture Notes in Computer Science, page 29-42. Springer, (2018)