Automated Filter Pruning Based on High-Dimensional Bayesian Optimization

Filter pruning is necessary to efficiently deploy convolutional neural networks on edge devices that have limited computational resources and power budgets. With conventional filter pruning techniques, the same pruning rate is manually specified for different convolutional layers, which is suboptimal and time-consuming. To extract the features from the coarse level to the fine level, the number of filters in each layer has various distributions. Therefore, it is unsuitable to utilize the same pruning rate for different functional layers. To address this issue, we propose a high-dimensional Bayesian optimization-based filter pruning (HDBOFP) algorithm, which aims to automatically determine the most appropriate pruning rate for each convolutional layer. In addition, the proposed method can automatically identify optimal pruning-rate combinations without a time-consuming retraining phase. Compared with conventional filter pruning methods, this automated filter pruning technique exhibits a higher efficiency, which improves accuracy and reduces the required human labor. The effectiveness of our automated filter pruning algorithm is validated through two major computer vision applications, namely image classification and object detection. Specifically, when used in combination with ResNet-110 to classify the CIFAR-10 dataset, HDBOFP reduces the required number of float point operations (FLOPs) by more than 62% without affecting accuracy. Similarly, when HDBOFP is added to the YOLOv5l framework to run detection experiments on the MS-COCO 2017 dataset, FLOPs decrease by more than 43% with only a 1.2% loss in mean average precision, which has advanced the previous studies.