Quantization and Training of Low Bit-Width Convolutional Neural Networks for Object Detection (Journal of Computational Mathematics, 2019)
We present LBW-Net, an efficient optimization based method for quantization andtraining of the low bit-width convolutional neural networks (CNNs). Specifically, we quantizethe weights to zero or powers of 2 by minimizing the Euclidean distance betweenfull-precision weights and quantized weights during backpropagation (weight learning).We characterize the combinatorial nature of the low bit-width quantization problem. For2-bit (ternary) CNNs, the quantization of $N$ weights can be done by an exact formulain $O$($N$ log $N$) complexity. When the bit-width is 3 and above, we further propose asemi-analytical thresholding scheme with a single free parameter for quantization that iscomputationally inexpensive. The free parameter is further determined by network retrainingand object detection tests. The LBW-Net has several desirable advantages overfull-precision CNNs, including considerable memory savings, energy efficiency, and fasterdeployment. Our experiments on PASCAL VOC dataset [5] show that compared with its32-bit floating-point counterpart, the performance of the 6-bit LBW-Net is nearly losslessin the object detection tasks, and can even do better in real world visual scenes, whileempirically enjoying more than 4× faster deployment.
Keywords: Quantization; Low bit width deep neural networks; Exact and approximate analytical formulas; Network training; Object detection.