论文标题
深度神经网络的计算复杂性降低
Computational complexity reduction of deep neural networks
论文作者
论文摘要
深度神经网络（DNN）已被广泛使用，并在计算机视觉和自主导航领域起着重要作用。但是，这些DNN在计算上是复杂的，并且在没有其他优化和自定义的情况下，它们在资源受限的平台上的部署很困难。 在本手稿中，我们描述了DNN体系结构的概述，并提出了降低计算复杂性的方法，以加速培训和推理速度，以使其适合具有低计算资源的边缘计算平台。
Deep neural networks (DNN) have been widely used and play a major role in the field of computer vision and autonomous navigation. However, these DNNs are computationally complex and their deployment over resource-constrained platforms is difficult without additional optimizations and customization. In this manuscript, we describe an overview of DNN architecture and propose methods to reduce computational complexity in order to accelerate training and inference speeds to fit them on edge computing platforms with low computational resources.
