摘要:深度神经网络（deep neural network，简称DNN）量化是一种高效的模型压缩方法，使用少量位宽表示模型计算过程中的参数和中间结果数据.数据位宽会直接影响内存占用、计算效率和能耗.以往的模型量化研究缺乏有效的定量分析，这导致量化损失难以预测.提出了一种超低损失的DNN量化方法（ultra-low loss quantization，简称μL2Q），以揭示量化位宽与量化损失之间的内在联系，指导量化位宽选择并降低量化损失.首先，将原始数据映射为标准正态分布的数据；然后，在等宽的量化区间中搜索最优量化参数；最后，将μL2Q方法融合进DNN的训练过程，并嵌入到主流的机器学习框架Caffe及Keras中，以支撑端到端模型压缩的设计和训练.实验结果表明，与最新的研究方法相比，在相同的位宽条件下，mL2Q方法能够保证更高的模型精度，在典型的神经网络模型上精度分别提高了1.94%，3.73%和8.24%.显著性物体检测实验结果表明，μL2Q方法能够胜任复杂的计算机视觉任务.



Abstract:Deep neural network (DNN) quantization is an efficient model compression method, in which parameters and intermediate results are expressed by low bit width. The bit width of data will directly affect the memory footprint, computing power and energy consumption. Previous researches on model quantization lack effective quantitative analysis, which leads to unpredictable quantization loss of these methods. This study proposes an ultra-low loss quantization (μL2Q) method for DNN compression, which reveals the internal relationship between quantization bit width and quantization loss, effectively guiding the selection of quantization bit width and reducing quantization loss. First, the original data is mapped to the data with standard normal distribution and then the optimal parameter configuration is sought to reduce the quantization loss under the target bit width. Finally, μL2Q has been encapsulated and integrated into two popular deep learning training frameworks, including Caffe and Keras, to support the design and training of end-to-end model compression. The experimental results show that compared with the state-of-the-art three clusters of quantization solutions, μL2Q can still guarantee the accuracy and deliver 1.94%, 3.73%, and 8.24% of accuracy improvements under the typical neural networks with the same quantization bit width, respectively. In addition, it is also verified that μL2Q can be competent for more complex computer vision tasks through salient object detection experiments.



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