快速卷积算法的综述研究

卷积神经网络是深度学习算法应用最广泛的方向之一，目前卷积神经网络的应用不仅仅是停留在科技领域，已经扩展到医学、军事等领域，并且已在相关领域发挥着巨大的作用。卷积是卷积神经网络中最为核心的一部分，卷积运算占整个网络70%以上的时间，所以针对卷积运算的加速研究就显得十分重要。首先介绍近年来的卷积算法，并对其复杂度进行分析，总结了这些算法各自的优点和不足，最后对其理论研究和应用领域可能存在的突破进行了探讨和展望。

A regional shared and high concurrent storagearchitecture based on NVMeoF storage pool

Convolutional neural network is one of the most widely applied directions of deep learning algorithms. At present, the application of convolutional neural network is not only in the field of science and technology, but also in medical, military and other fields, and has played a huge role in related fields. Convolution is the most core part of convolutional neural network, and the computation amount of convolution accounts for more than 70% of the time of the whole network. Therefore, it is very important to study the acceleration of convolution operation. Firstly, the convolution algorithms in recent years are introduced, and their complexity is analyzed. The advantages and disadvantages of these algorithms are summarized. Finally, the possible breakthroughs in theoretical research and application are discussed and prospected.