RNNPool: Efficient Non-linear Pooling for RAM Constrained Inference

Pooling operators are key components in most Convolutional Neural Networks (CNNs) as they serve to downsample images, aggregate feature information, and increase receptive field. However, standard pooling operators reduce the feature size gradually to avoid significant loss in information via gross aggregation. Consequently, CNN architectures tend to be deep, computationally expensive and challenging to deploy on RAM constrained devices. We introduce RNNPool, a novel pooling operator based on Recurrent Neural Networks (RNNs), that efficiently aggregate features over large patches of an image and rapidly downsamples its size. Our empirical evaluation indicates that an RNNPool layer(s) can effectively replace multiple blocks in a variety of architectures such as MobileNets (Sandler et al., 2018), DenseNet (Huang et al., 2017) and can be used for several vision tasks like image classification and face detection. That is, RNNPool can significantly decrease computational complexity and peak RAM usage for inference, while retaining comparable accuracy. Further, we use RNNPool to construct a novel real-time face detection method that achieves state-of-the-art MAP within computational budget afforded by a tiny Cortex M4 microcontroller with ~256 KB RAM.