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QUANTIZATION AND TRAINING OF LOW BIT-WIDTH CONVOLUTIONAL NEURAL NETWORKS FOR OBJECT DETECTION

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摘要 We presen t LBW-Net,an efficient optimization based method for qua nt ization and training of the low bit-width convolutional neural networks(CNNs).Specifically,we quantize the weights to zero or powers of 2 by minimizing the Euclidean distance between full-precision weights and quantized weights during backpropagation(weight learning).We characterize the combinatorial nature of the low bit-width quantization problem.For 2-bit(ternary)CNNs,the quantization of N weights can be done by an exact formula in O(N log N)complexity.When the bit-width is 3 and above,we further propose a semi-analytical thresholding scheme with a single free parameter for quantization that is computationally inexpensive.The free parameter is further determined by network retraining and object detection tests.The LBW-Net has several desirable advantages over full-precision CNNs,including considerable memory savings,energy efficiency,and faster deployment.Our experiments on PASCAL VOC dataset show that compared with its 32-bit floating-point counterpart,the performance of the 6-bit LBW-Net is nearly lossless in the object detection tasks,and can even do better in real world visual scenes,while empirically enjoying more than 4× faster deployment.
出处 《计算数学:英文版》 SCIE CSCD 2019年第3期349-359,共11页 Journal of Computational Mathematics
基金 NSF grants DMS-1522383,IIS-1632935 ONR grant N00014-16-1-2157.
作者简介 Penghang Yin,Email:yph@ucla.edu;Shuai Zhang,Email:szhang3@uci.edu;Yingyong Qi,Email:yqi@uci.edu;Jack Xin,Email:jack.xin@uci.edu.
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