A Surrogate Lagrangian Relaxation-based Model Compression for Deep Neural Networks

Network pruning is a widely used technique to reduce computation cost and model size for deep neural networks. However, the typical three-stage pipeline, i.e., training, pruning and retraining (fine-tuning) significantly increases the overall training trails. For instance, the retraining process could take up to 80 epochs for ResNet-18 on ImageNet, that is 70% of the original model training trails. In this paper, we develop a systematic weight-pruning optimization approach based on Surrogate Lagrangian relaxation (SLR), which is tailored to overcome difficulties caused by the discrete nature of the weight-pruning problem while ensuring fast convergence. We decompose the weight-pruning problem into subproblems, which are coordinated by updating Lagrangian multipliers. Convergence is then accelerated by using quadratic penalty terms. We evaluate the proposed method on image classification tasks, i.e., ResNet-18, ResNet-50 and VGG-16 using ImageNet and CIFAR-10, as well as object detection tasks, i.e., YOLOv3 and YOLOv3-tiny using COCO 2014, PointPillars using KITTI 2017, and Ultra-Fast-Lane-Detection using TuSimple lane detection dataset. Numerical testing results demonstrate that with the adoption of the Surrogate Lagrangian Relaxation method, our SLR-based weight-pruning optimization approach achieves a high model accuracy even at the hard-pruning stage without retraining for many epochs, such as on PointPillars object detection model on KITTI dataset where we achieve 9.44x compression rate by only retraining for 3 epochs with less than 1% accuracy loss. As the compression rate increases, SLR starts to perform better than ADMM and the accuracy gap between them increases. SLR achieves 15.2% better accuracy than ADMM on PointPillars after pruning under 9.49x compression. Given a limited budget of retraining epochs, our approach quickly recovers the model accuracy.