Recursive least squares method for training and pruning convolutional neural networks

Abstract

Convolutional neural networks (CNNs) have shown good performance in many practical applications. However, their high computational and storage requirements make them difficult to deploy on resource-constrained devices. To address this issue, in this paper, we propose a novel iterative structured pruning algorithm for CNNs based on the recursive least squares (RLS) optimization. Our algorithm combines inverse input autocorrelation matrices with weight matrices to evaluate and prune unimportant input channels or nodes in each CNN layer and performs the next pruning operation when the testing loss is tuned down to the last unpruned level. Our algorithm can be used to prune feedforward neural networks (FNNs) as well. The fast convergence speed of the RLS optimization allows our algorithm to prune CNNs and FNNs multiple times in a small number of epochs. We validate its effectiveness in pruning VGG-16 and ResNet-50 on CIFAR-10 and CIFAR-100 and pruning a three-layer FNN on MNIST. Compared with four popular pruning algorithms, our algorithm can adaptively prune CNNs according to the learning task difficulty and can effectively prune CNNs and FNNs with a small or even no reduction in accuracy. In addition, our algorithm can prune the original sample features in the input layer.