Abstract
Deep convolutional neural networks (CNNs) are widely used for image classification. Deep CNNs often require a large memory and abundant computation resources, limiting their usability in embedded or mobile devices. To overcome this limitation, several pruning methods have been proposed. However, most of the existing methods focus on pruning parameters and cannot efficiently address the computation costs of deep CNNs. Additionally, these methods ignore the connections between the feature maps of different layers. This paper proposes a multi-objective pruning based on feature map selection (MOP-FMS). Unlike previous studies, we use the number of floating point operations (FLOPs) as a pruning objective in addition to the accuracy of the pruned network. First, we propose an encoding method based on feature map selection with a compact and efficient search space. Second, novel domain-specific crossover and mutation operators with reparation are designed to generate new individuals and make them meet the constraint rules. Then, decoding and pruning methods are proposed to prune networks based on the results of feature map selection. Finally, multi-objective optimisation is used for evaluation and individual selection. Our method has been tested with commonly used network structures. Numerical results demonstrate that the proposed method achieves better results than other state-of-the-art methods in terms of pruning rate without decreasing the accuracy rate to a high degree.
•Considering the relation between the feature maps from different layers, the pruning problem is formulated as a bi-objective optimisation problem with feature map selection, and the accuracy rate and computation cost are simultaneously optimised.•A novel feature map-based encoding method and a unique decoding method are proposed for pruning common structures or networks with additive aggregation.•Special initialisation, crossover and mutation operators are designed with the quick reparation method to satisfy the encoding constraints of this specific problem.