| dc.description.abstract | The CNN-XGB architecture combines the feature extraction capabilities of Convolutional Neural Networks (CNN) with the classification power of XGBoost. Many studies have shown that CNN-XGB outperforms using CNN or XGBoost alone. However, deep CNNs can lead to increased computation time. To address this issue, some researchers have pruned the tail end of the CNN layers, attempting to allow XGBoost to replace these functions. However, they have also found that this can lead to a decrease model’s performance. This study proposes a CNN-XGB architecture with low hardware resource requirement. Unlike other studies, we have reduced even more layers from the CNN and utilized image feature algorithms such as Local Binary Pattern (LBP) and Histogram of Oriented Gradients (HOG) to assist the CNN, providing more feature data to the XGBoost classifier. This approach aims to prevent significant performance drops despite using a deeply pruned CNN. In our experimental design, we gradually reduce the number of CNN layers and observe the changes in efficiency and performance. Additionally, we have developed an automated program to quickly deploy the XGBoost model from software to hardware. Our experimental results confirm that although pruning the CNN causes a 1-5% drop in the CNN-XGB recognition rate, computation time and storage resources can be reduced by 10-25% and 40-80%, respectively. In multimodal CNN-XGB experiments, using multimodal enhancement, some results show that the performance of CNN-XGB can recover to the level of the unpruned model while maintaining the efficiency gains brought by low resource usage. In experiments on the hardware implementation of XGBoost, results verify that the XGBoost model can be successfully deployed on hardware. Although the accuracy drops by 1-6%, the computation speed can increase by 24 to 32 times compared to the software implementation. In the future, we aim to complete the hardware design for the CNN part and connect it with the XGBoost hardware design developed in this study. This will enable the proposed low resource requirement CNN-XGB classifier to be fully implemented on hardware, contributing to advancements in the relevant fields. | en_US |