Leveraging large data sets, deep Convolutional Neural Networks (CNNs) achieve state-of-the-art recognition accuracy. Due to the substantial compute and memory operations, however, they require significant execution time. The massive parallel computing capability of GPUs make them as one of the ideal platforms to accelerate CNNs and a number of GPU-based CNN libraries have been developed. While existing works mainly focus on the computational efficiency of CNNs, the memory efficiency of CNNs have been largely overlooked. Yet CNNs have intricate data structures and their memory behavior can have significant impact on the performance. In this work, we study the memory efficiency of various CNN layers and reveal the performance implication from both data layouts and memory access patterns. Experiments show the universal effect of our proposed optimizations on both single layers and various networks, with up to 27.9x for a single layer and up to 5.6x on the whole networks.

本文探讨了对于卷积神经网络的内存效率的优化，以及其对性能的影响，并提出了数据布局及内存访问模式方面的优化策略。实验证明，这些策略不仅对于单个层面有效，对于整个神经网络也可获得高达27.9倍和5.6倍的速度提升。

在GPU上优化深度卷积神经网络的内存效率