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High Performance Implementation of 3D Convolutional Neural Networks on a GPU

机译：在GPU上实现3D卷积神经网络的高性能实现

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Convolutional neural networks have proven to be highly successful in applications such as image classification, object tracking, and many other tasks based on 2D inputs. Recently, researchers have started to apply convolutional neural networks to video classification, which constitutes a 3D input and requires far larger amounts of memory and much more computation. FFT based methods can reduce the amount of computation, but this generally comes at the cost of an increased memory requirement. On the other hand, the Winograd Minimal Filtering Algorithm (WMFA) can reduce the number of operations required and thus can speed up the computation, without increasing the required memory. This strategy was shown to be successful for 2D neural networks. We implement the algorithm for 3D convolutional neural networks and apply it to a popular 3D convolutional neural network which is used to classify videos and compare it to cuDNN. For our highly optimized implementation of the algorithm, we observe a twofold speedup for most of the 3D convolution layers of our test network compared to the cuDNN version.
机译：卷积神经网络已被证明在诸如图像分类，对象跟踪以及基于2D输入的许多其他任务等应用中非常成功。最近，研究人员已开始将卷积神经网络应用于视频分类，视频分类构成了3D输入，并且需要大量的内存和更多的计算量。基于FFT的方法可以减少计算量，但这通常以增加内存需求为代价。另一方面，Winograd最小过滤算法（WMFA）可以减少所需的操作数量，从而可以加快计算速度，而无需增加所需的内存。对于二维神经网络，该策略已被证明是成功的。我们为3D卷积神经网络实现了该算法，并将其应用于流行的3D卷积神经网络，该网络用于对视频进行分类并将其与cuDNN进行比较。对于我们的算法的高度优化实现，与cuDNN版本相比，我们观察到了测试网络中大多数3D卷积层的两倍加速。

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