## 5 Conclusions and Future Work  
In this paper, we propose a simple yet effective variable rate filter pruning method for deep CNN acceleration, named MSVFP. The proposed method utilizes both magnitude and similarity-based filter selection criteria while preventing abrupt accuracy loss during pruning. The optimal set of filters in terms of model overall loss is selected and pruned from the specific layer of the network until the target pruning rate is achieved. The pruning algorithm is designed in such a way that in every pruning iteration pruning CNN layer and the set of filters to be pruned are automatically selected. Consequently, instead of  
Figure 3: Validation accuracy while pruning the baseline ResNet-18 model on the ImageNet dataset across different FLOPs reduction rates. After every 3% FLOPs reduction, fine-tuning is performed for one epoch to prevent the accuracy drop.  
Figure 2: Validation accuracy across training epochs while baseline training and fine-tuning the pruned ResNet-18 model on the ImageNet dataset.  
fixed rate pruning, at the end of the pruning process, filters from each layer with different pruning rates are removed. The pruning results on the small and large-scale benchmark datasets with different CNN architectures show that MSVFP advances several popular state-of-the-art methods. In the case of VGG-16 pruning on the CIFAR-10 dataset, even after pruning more than 50% of the network FLOPs, there is no accuracy drop. Again, while pruning ResNet-56 and ResNet-110 on the CIFAR-10 dataset with more than 52% FLOPs reduction rate, the top-1 accuracy of the pruned model even advances by 0.11% and 0.23%, respectively. Also, while pruning the ResNet-50 model on the ImageNet benchmark dataset with more than 53% FLOPs reduction rate, there is only 1.11% top-1 and 0.55% top-5 accuracy drop w.r.t. baseline accuracy.  
In the future, we will focus on replacing the traditional train-prune-retrain method used in this paper with a more advanced pruning-while-training technique utilizing a similar filter selection strategy. Again, although we tried to minimize computation cost using a small subset of the training dataset for loss estimation due to pruning, it will be better to completely remove the involvement of the training dataset during the pruning process. In the proposed algorithm we manually partition the whole filter selection criteria among the magnitude criteria and similarity criteria. In future work, instead of manual partitioning, we may also consider the automatic selection of optimal filter selection criteria from the pool of available criteria, as different layers have various peculiarities and the distribution of filters across layers and networks with various benchmark datasets is also different. Moreover, it will be interesting to see the effectiveness of the proposed method on more advanced CNN applications such as object detection, image segmentation, face recognition, etc.  
Conceptualization, D.G.; methodology, D.G.; software, D.G.; validation, D.G., and S.-H.K.; resources, S.-H.K.; data curation, D.G.; writing--original draft preparation, D.G.; writing--review and editing, D.G. and S.-H.K.; visualization, D.G.; supervision, S.-H.K.; project administration, S.-H.K.; funding acquisition, S.-H.K. All authors have read and agreed to the published version of the manuscript.  
This work was supported by a National Research Foundation of Korea (NRF) grant funded by the Korean government(MSIT) (No. NRF-2021R1A4A1032252).  
Not applicable.  
The authors declare no conflict of interest.