3D convolution neural networks (CNNs) have shown excellent predictive performance on tasks such as action recognition from videos. Since 3D CNNs have unique characteristics and extremely high compute/memory-overheads, executing them on accelerators designed for 2D CNNs provides sub-optimal performance. To overcome these challenges, researchers have recently proposed architectures for 3D CNNs. In this paper, we present a survey of hardware accelerators and hardware-aware algorithmic optimizations for 3D CNNs. We include only those CNNs that perform 3D convolution and not those that perform only 2D convolution on 2D or 3D data We highlight their key ideas and underscore their similarities and differences. We believe that this survey will spark a great deal of research towards the design of ultra-efficient 3D CNN accelerators of tomorrow.

3D卷积神经网络（CNN）对诸如视频的动作识别之类的任务显示出出色的预测性能。由于3D CNN具有独特的特性和极高的计算/内存开销，因此在为2D CNN设计的加速器上执行它们会带来次佳的性能。为了克服这些挑战，研究人员最近提出了3D CNN的体系结构。在本文中，我们对3D CNN的硬件加速器和硬件感知算法优化进行了概述。我们仅包括那些对3D卷积执行3D卷积的CNN，而不包括那些仅对2D或3D数据进行2D卷积的CNN。我们重点介绍其关键思想并强调它们的异同。我们相信，这项调查将引发有关未来超高效3D CNN加速器设计的大量研究。