基于dnn的高效图像传感器子采样图像分类

Jiaxian Guo, Hongxiang Gu, M. Potkonjak
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引用次数: 6

摘要

今天的移动设备都配备了能够拍摄高分辨率照片的摄像头。对于要求相对较低分辨率的计算机视觉任务,如图像分类,需要进行子采样以减少图像传感器的不必要功耗。本文研究了基于深度神经网络(dnn)的图像分类器的子采样与性能下降之间的关系。我们的经验表明,具有相同步长的子采样导致不同分类器非常相似的精度变化。特别是,我们可以通过子采样实现超过15倍的能源节约,同时几乎没有准确性损失。为了更好地权衡能量精度,我们提出了AdaSkip,其中行采样分辨率根据图像梯度自适应改变。我们在FPGA上实现了AdaSkip,并报告了其能耗。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Efficient Image Sensor Subsampling for DNN-Based Image Classification
Today's mobile devices are equipped with cameras capable of taking very high-resolution pictures. For computer vision tasks which require relatively low resolution, such as image classification, sub-sampling is desired to reduce the unnecessary power consumption of the image sensor. In this paper, we study the relationship between subsampling and the performance degradation of image classifiers that are based on deep neural networks (DNNs). We empirically show that subsampling with the same step size leads to very similar accuracy changes for different classifiers. In particular, we could achieve over 15x energy savings just by subsampling while suffering almost no accuracy lost. For even better energy accuracy trade-offs, we propose AdaSkip, where the row sampling resolution is adaptively changed based on the image gradient. We implement AdaSkip on an FPGA and report its energy consumption.
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