Standard Convolutional Neural Networks (CNNs) designed for computer vision
tasks tend to have large intermediate activation maps. These require large
working memory and are thus unsuitable for deployment on resource-constrained
devices typically used for inference on the edge. Aggressively downsampling the
images via pooling or strided convolutions can address the problem but leads to
a significant decrease in accuracy due to gross aggregation of the feature map
by standard pooling operators. In this paper, we introduce RNNPool, a novel
pooling operator based on Recurrent Neural Networks (RNNs), that efficiently
aggregates features over large patches of an image and rapidly downsamples
activation maps. Empirical evaluation indicates that an RNNPool layer can
effectively replace multiple blocks in a variety of architectures such as
MobileNets, DenseNet when applied to standard vision tasks like image
classification and face detection. That is, RNNPool can significantly decrease
computational complexity and peak memory usage for inference while retaining
comparable accuracy. We use RNNPool with the standard S3FD architecture to
construct a face detection method that achieves state-of-the-art MAP for tiny
ARM Cortex-M4 class microcontrollers with under 256 KB of RAM. Code is released
at this https URL

介绍了一种新型的池化算子 RNNpool—— 基于循环神经网络，可在大的图像区域上高效地聚合特征，快速下采样激活映射。在标准视觉任务中应用于 MobileNets、DenseNet 等多种体系结构，可显著降低推理的计算复杂度和峰值内存使用量，保持可比的准确性。使用 RNNpool 与标准 S3FD 体系结构构建了一种面部检测方法，在 256 KB RAM 以下的微型控制器上实现了最先进的 MAP。