论文标题
无处不在的像素无处不在：查找并排除它们以提高计算机视觉效率
Irrelevant Pixels are Everywhere: Find and Exclude Them for More Efficient Computer Vision
论文作者
论文摘要
通常使用卷积神经网络（CNN）进行计算机视觉。 CNN是计算密集型的，并且在移动和图像（IoT）设备等电力控制系统上部署。 CNN是计算密集型的，因为它们在输入图像的所有像素上都不可差计算许多特征。我们观察到，鉴于计算机视觉任务，图像通常包含与任务无关的像素。例如，如果任务正在寻找汽车，那么天空中的像素不是很有用。因此，我们建议对CNN进行修改以仅在相关像素上操作以节省计算和能量。我们提出了一种研究三个流行的计算机视觉数据集的方法，发现48％的像素无关紧要。我们还建议集中卷积修改CNN的卷积层，以拒绝标记无关的像素。在嵌入式设备上，我们观察到准确性没有损失，而推断潜伏期，能耗和乘以add计数均降低了约45％。
Computer vision is often performed using Convolutional Neural Networks (CNNs). CNNs are compute-intensive and challenging to deploy on power-contrained systems such as mobile and Internet-of-Things (IoT) devices. CNNs are compute-intensive because they indiscriminately compute many features on all pixels of the input image. We observe that, given a computer vision task, images often contain pixels that are irrelevant to the task. For example, if the task is looking for cars, pixels in the sky are not very useful. Therefore, we propose that a CNN be modified to only operate on relevant pixels to save computation and energy. We propose a method to study three popular computer vision datasets, finding that 48% of pixels are irrelevant. We also propose the focused convolution to modify a CNN's convolutional layers to reject the pixels that are marked irrelevant. On an embedded device, we observe no loss in accuracy, while inference latency, energy consumption, and multiply-add count are all reduced by about 45%.
