Model Efficiency Improvement with Simplified Architectures

In previous lightweight methods, whether in the design of lightweight model architectures or in model compression and acceleration techniques, most design approaches focused on reducing the number of parameters as the primary objective. However, reducing the number of parameters does not always guarantee improved acceleration. Sometimes, the two factors are complementary, and during the process of reducing parameters, there is a simultaneous decrease in accuracy. Therefore, achieving a balance between efficiency and accuracy has always been an important issue in lightweight methods. This paper employs a different approach than previous model compression and acceleration methods. Instead of continuously reducing the number of parameters to achieve faster model inference, it optimizes the algorithm of the model inference stage and the overall complexity of the architecture. This approach ensures that the model does not incur additional inference time costs while maintaining the original architectural performance, thereby improving model inference speed without sacrificing accuracy.