Deep convolutional neural networks have produced excellent results when utilized for image classification tasks, and they are being applied in a growing number of contexts. Model inference on edge devices is challenging due to the unending complicated structures needed to improve performance, which adds a significant computing burden.According to recent research, the often utilized residual structure in models does not support model inference. The idea of structural reparameterization is put out to address this shortcoming. The RepVGG produced with this method is a high-performance, quick-inference single-path network. Even after reparameterization, the model still needs GPUs and other specialized computing libraries to accelerate inference, however this still has a limit on how quickly the model can infer at the edge. We construct RDPNet using depthwise separable convolution and structural reparameterization to further reduce model size and accelerate inference. When utilizing an Intel CPU, this is a straightforward network that may be utilized for inference. For re-parameterization, we specifically adopt Depthwise separable convolution as the basic convolution form. Create a multi-branch model for training on the training side, and then simplify it into a single-branch model that the edge devices can easily infer. Research demonstrates that compared to alternative lightweight networks that can attain SOTA performance, RDPNet offers a superior trade-off between accuracy and latency.