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Highly Efficient 8-bit Low Precision Inference of Convolutional Neural Networks with IntelCaffe

机译：卷积神经网络的高效8位低精度推断与英特拉夫蔓作用

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Jiong Gong; Haihao Shen; Guoming Zhang; Xiaoli Liu; Shane Li; Ge Jin; Niharika Maheshwari; Evarist Fomenko; Eden Segal;
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2. A Resource-Efficient Inference Accelerator for Binary Convolutional Neural Networks [J] . Kim Tae-Hwan, Shin Jihoon Circuits and Systems II: Express Briefs, IEEE Transactions on . 2021,第1期

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3. Efficient adaptive inference for deep convolutional neural networks using hierarchical early exits [J] . Passalis Nikolaos, Raitoharju Jenni, Tefas Anastasios, Pattern Recognition: The Journal of the Pattern Recognition Society . 2020,第期

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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

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5. Caffeinated FPGAs: FPGA Framework for Training and Inference of Convolutional Neural Networks With Reduced Precision Floating-Point Arithmetic [D] . DiCecco, Roberto. 2018

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6. Deep convolutional neural networks in the classification of dual-energy thoracic radiographic views for efficient workflow: analysis on over 6500 clinical radiographs [O] . Jennie Crosby, Thomas Rhines, Feng Li, 2020

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7. Lance: efficient low-precision quantized winograd convolution for neural networks based on graphics processing units [O] . Guangli Li, Lei Liu, Xueying Wang, 2020

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Highly Efficient 8-bit Low Precision Inference of Convolutional Neural Networks with IntelCaffe

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