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

• 来源期刊: 计算数学（英文版）
• 关键词: Quantization Low bit width deep neural networks Exact and approximate analytical formulas Network training Object detection
• 年，卷（期）: 2019,（3）
• 页码范围: 349-359
• 页数: 11页
• 语种: 英文
• DOI: 10.4208/jcm.1803-m2017-0301

摘要

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(Nlog 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.