结合半波高斯量化与交替更新的神经网络压缩方法

为使神经网络模型能在实时性要求较高且内存容量受限的边缘设备上部署使用，提出一种基于半波高斯量化与交替更新的混合压缩方法。对神经网络模型输入部分进行2 bit均匀半波高斯量化，将量化值输入带有缩放因子的二值网络通过训练得到初始二值模型，利用交替更新方法对已训练的二值模型进行逐层微调以提高模型测试精度。在CIFAR-10和ImageNet数据集上的实验结果表明，该方法能有效降低参数和结构冗余所导致的内存和时间开销，在神经网络模型压缩比接近30的前提下，测试精度相比HWGQ-Net方法提高0.8和2.0个百分点且实现了10倍的训练加速。

Neural Network Compression Method Combining Half-Wave Gaussian Quantization and Alternate Update

To enable the deployment of neural network models on edge devices with a limited memory size and high real-time performance requirements,this paper proposes a hybrid compression method combining Half-Wave Gaussian Quantization(HWGQ)and alternate update.By performing the 2 bit uniform HWGQ on the input of the neural network model,the quantized value is input into a binary network with a scaling factor,which is trained to obtain the initial binary model.Then the trained binary model is fine-tuned layer by layer using the alternating update method to improve the accuracy of the model.Experimental results on the CIFAR-10 and ImageNet datasets show that the proposed method significantly reduces the memory consumption and time consumption caused by parameter redundancy and structural redundancy.When the model compression ratio is about 30,the accuracy of the model is increased by 0.8 and 2.0 percentage points compared with that of the HWGQ-Net method,and its training speed is increased by 10 times.