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Lance: efficient low-precision quantized winograd convolution for neural networks based on graphics processing units

机译：LANCE：基于图形处理单元的神经网络有效低精度量化Winograd卷积

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Guangli Li; Lei Liu; Xueying Wang; Xiu Ma; Xiaobing Feng;
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2. O⁴-DNN: A Hybrid DSP-LUT-Based Processing Unit With Operation Packing and Out-of-Order Execution for Efficient Realization of Convolutional Neural Networks on FPGA Devices [J] . Haghi Pouya, Kamal Mehdi, Afzali-Kusha Ali, IEEE transactions on circuits and systems . I , Regular papers . 2020,第9期

机译：O⁴-DNN：一种基于混合DSP-LUT的处理单元，具有操作包装和超出执行，以便在FPGA设备上有效地实现卷积神经网络
3. High Performance and Efficient Real-Time Face Detector on Central Processing Unit Based on Convolutional Neural Network [J] . Putro Muhamad Dwisnanto, Kurnianggoro Laksono, Jo Kang-Hyun IEEE transactions on industrial informatics . 2021,第7期

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4. Lance: Efficient Low-Precision Quantized Winograd Convolution for Neural Networks Based on Graphics Processing Units [C] . Guangli Li, Lei Liu, Xueying Wang, IEEE International Conference on Acoustics, Speech and Signal Processing . 2020

机译：Lance：基于图形处理单元的神经网络高效低精度量化Winograd卷积
5. Fast Convolutional Neural Networks on Graphics Processing Units [D] . Zhang, Yulin . 2019

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6. Efficient implementation of convolutional neural networks in the data processing of two-photon in vivo imaging [O] . Yangzhen Wang, Feng Su, Shanshan Wang, -1

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7. CNN-MERP: An FPGA-Based Memory-Efficient Reconfigurable Processor for Forward and Backward Propagation of Convolutional Neural Networks [O] . Han, Xushen, Zhou, Dajiang, Wang, Shihao, 2017

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Lance: efficient low-precision quantized winograd convolution for neural networks based on graphics processing units

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