# Accelerating CNN Training by Pruning Activation Gradients Source: [Accelerating CNN Training by Pruning Activation Gradients]() Collection index: [llms.txt]() Data updated: 2025-10-25T22:27:00.192Z Authors: Xucheng Ye, Pengcheng Dai, Junyu Luo, Xin Guo, Yingjie Qi, Jianlei Yang, Yiran Chen Published: 2019-08-01T01:48:11.000Z ## Abstract Sparsification is an efficient approach to accelerate CNN inference, but it is challenging to take advantage of sparsity in training procedure because the involved gradients are dynamically changed. Actually, an important observation shows that most of the activation gradients in back-propagation are very close to zero and only have a tiny impact on weight-updating. Hence, we consider pruning these very small gradients randomly to accelerate CNN training according to the statistical distribution of activation gradients. Meanwhile, we theoretically analyze the impact of pruning algorithm on the convergence. The proposed approach is evaluated on AlexNet and ResNet-\\{18, 34, 50, 101, 152\\} with CIFAR-\\{10, 100\\} and ImageNet datasets. Experimental results show that our training approach could substantially achieve up to $5.92 \\times$ speedups at back-propagation stage with negligible accuracy loss. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()