Feature Map Analysis-Based Dynamic CNN Pruning and the Acceleration on FPGAs

Abstract

Deep-learning-based applications bring impressive results to graph machine learning and are widely used in fields such as autonomous driving and language translations. Nevertheless, the tremendous capacity of convolutional neural networks makes it difficult for them to be implemented on resource-constrained devices. Channel pruning provides a promising solution to compress networks by removing a redundant calculation. Existing pruning methods measure the importance of each filter and discard the less important ones until reaching a fixed compression target. However, the static approach limits the pruning effect. Thus, we propose a dynamic channel-pruning method that dynamically identifies and removes less important filters based on a redundancy analysis of its feature maps. Experimental results show that 77.10% of floating-point operations per second (FLOPs) and 91.72% of the parameters are reduced on VGG16BN with only a 0.54% accuracy drop. Furthermore, the compressed models were implemented on the field-programmable gate array (FPGA) and a significant speed-up was observed.

Is My Pruned Model Trustworthy? PE-Score: A New CAM-Based Evaluation Metric

“…Accuracy (i.e., final accuracy, accuracy drop) and other similar metrics, such as the F1-score, are often used to evaluate the performance. The model size is measured as a function of the number of parameters, while the computational cost is determined by the number of FLOPs (floating point operations) [11][12][13][14]. In this way, the reduction results of the pruned model with respect to the unpruned model can be quantified, taking into account that the smaller the reduction in accuracy (or the smaller the increase in error) and the greater the reduction in both parameters and FLOPs, the better the pruned model.…”