( en | de | ru )

 

Disambiguation

From post

копироватьудалитьдобавить публикацию в буфер
FINN-R: An End-to-End Deep-Learning Framework for Fast Exploration of Quantized Neural Networks.

, , , , , и . CoRR, (2018)

Please choose a person to relate this publication to

To differ between persons with the same name, the academic degree and the title of an important publication will be displayed.

Nicholas J Eisner

Nicholas J Moore

Otto Fraser

Anke Fraser

Keir Fraser

 

Другие публикации лиц с тем же именем

FINN-R: An End-to-End Deep-Learning Framework for Fast Exploration of Quantized Neural Networks., , , , , , , и . ACM Trans. Reconfigurable Technol. Syst., 11 (3): 16:1-16:23 (2018)SYQ: Learning Symmetric Quantization for Efficient Deep Neural Networks., , , и . CVPR, стр. 4300-4309. Computer Vision Foundation / IEEE Computer Society, (2018)Trainable Preprocessing for Reduced Precision Neural Networks., , , , и . EUSIPCO, стр. 1546-1550. IEEE, (2021)Kernel Normalised Least Mean Squares with Delayed Model Adaptation., и . ACM Trans. Reconfigurable Technol. Syst., 13 (2): 7:1-7:30 (2020)Accuracy to Throughput Trade-Offs for Reduced Precision Neural Networks on Reconfigurable Logic., , , , , , , и . ARC, том 10824 из Lecture Notes in Computer Science, стр. 29-42. Springer, (2018)Distributed kernel learning using Kernel Recursive Least Squares., , , и . ICASSP, стр. 5500-5504. IEEE, (2015)Compressing Low Precision Deep Neural Networks Using Sparsity-Induced Regularization in Ternary Networks., , , , и . ICONIP (2), том 10635 из Lecture Notes in Computer Science, стр. 393-404. Springer, (2017)FINN-R: An End-to-End Deep-Learning Framework for Fast Exploration of Quantized Neural Networks., , , , , и . CoRR, (2018)High-Throughput DNN Inference with LogicNets., , , и . FCCM, стр. 238. IEEE, (2020)Braiding: A scheme for resolving hazards in kernel adaptive filters., , , и . FPT, стр. 136-143. IEEE, (2015)