Computer vision often uses highly accurate Convolutional Neural Networks
(CNNs), but these deep learning models are associated with ever-increasing
energy and computation requirements. Producing more energy-efficient CNNs often
requires model training which can be cost-prohibitive. We propose a novel,
automated method to make a pretrained CNN more energy-efficient without
re-training. Given a pretrained CNN, we insert a threshold layer that filters
activations from the preceding layers to identify regions of the image that are
irrelevant, i.e. can be ignored by the following layers while maintaining
accuracy. Our modified focused convolution operation saves inference latency
(by up to 25%) and energy costs (by up to 22%) on various popular pretrained
CNNs, with little to no loss in accuracy.

通过插入一个阈值层来过滤图片中的无关区域，从而使预训练的深度卷积神经网络更加节能、减少计算需求，并在准确率几乎不下降的情况下节省推断延迟和能源消耗。

利用聚焦卷积消除无关像素提高预训练 CNN 推理延迟和能效的自动化方法

An automated approach for improving the inference latency and energy  efficiency of pretrained CNNs by removing irrelevant pixels with focused  convolutions