If a side-channel attack can attack the structure and framework of the neural network to recover information such as structure and weight, sensitive information leakage will occur. Therefore, it is important to guard the neural network computing devices against disclosure of sensitive information in the field of side-channel attack. Based on the Jetson Nano platform, a method is designed for the side-channel electromagnetic leakage signal acquisition during the inference of the neural network. The side-channel analysis is conducted by using the deep learning method, and two aspects of security are assessed. Research shows that a good network conversion strategy can improve the classification and recognition accuracy of the network by 5%~12%. In the two evaluation tasks, the classification accuracy of electromagnetic leakage is 97.21% for typical neural network inferences with different structures under the same framework; it reaches 100% for the same kind of network reasoning under different frameworks of neural network. It indicates that the side-channel electromagnetic attack method poses a threat to the privacy of deep learning algorithms in such embedded Graphics Processing Unit(GPU) computing platforms.