TY - JOUR
T1 - Quantization and Training of Low Bit-Width Convolutional Neural Networks for Object Detection
AU - Yin , Penghang
AU - Zhang , Shuai
AU - Qi , Yingyong
AU - Xin , Jack
JO - Journal of Computational Mathematics
VL - 3
SP - 349
EP - 359
PY - 2018
DA - 2018/09
SN - 37
DO - http://doi.org/10.4208/jcm.1803-m2017-0301
UR - https://global-sci.org/intro/article_detail/jcm/12726.html
KW - Quantization, Low bit width deep neural networks, Exact and approximate analytical formulas, Network training, Object detection.
AB - <p style="text-align: justify;">We present LBW-Net, an efficient optimization based method for quantization 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 [5] 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.</p>