Thanks for Nothing: Predicting Zero-Valued Activations with Lightweight Convolutional Neural Networks
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Convolutional neural networks (CNNs) introduce state-of-the-art results for various tasks with the price of high computational demands. Inspired by the observation that spatial correlation exists in CNN output feature maps (ofms), we propose a method to dynamically predict whether ofm activations are zero-valued or not according to their neighboring activation values, thereby avoiding zero-valued activations and reducing the number of convolution operations. We implement the zero activation predictor (ZAP) with a lightweight CNN, which imposes negligible overheads and is easy to deploy and train. Furthermore, the same ZAP can be tuned to many different operating points along the accuracy-savings trade-off curve. For example, using VGG-16 and the ILSVRC-2012 dataset, different operating points achieve a reduction of 23.5 and 32.3 degradation of 0.3 Considering one-epoch fine-tuning, 41.7 1.1
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