关键词: 深度卷积神经网络, 模型训练, 多层融合, 批归一化重构, 访存优化

Abstract:

Batch Normalization (BN) can effectively speed up deep neural network training, while its complex data dependence leads to the serious "memory wall" bottleneck. Aiming at the "memory wall" bottleneck for the training of the convolutional neural network(CNN) with BN layers, an effective memory access optimization method is proposed through BN reconstruction and fused-layers computation. First, through detailed analysis of BN’s data dependence and memory access features during training, some key factors for large amounts of memory access are identified. Second, the “Convolution + BN + ReLU (Rectified Linear Unit)” block is fused as a computational block to reduce memory access with re-computing strategy in training. Besides, the BN layer is split into two sub-layers which are respectively fused with its adjacent layers, and this approach further reduces memory access during training and effectively improves the accelerator’s computational efficiency. Experimental results show that the amount of memory access is decreased by 33%, 22% and 31% respectively, and the actual computing efficiency of the V100 is improved by 20.5%, 18.5% and 18.1% respectively when the ResNet-50, Inception V3 and DenseNet are trained on the NVIDIA TELSA V100 GPU with the optimization method. The proposed method exploits the characteristics of memory access during training, and can be used in conjunction with other optimization methods to further reduce the amount of memory access during training.

Key words: deep convolutional neural networks, model training, fused-layers, batch normalization reconstruction, off-chip memory access optimization.