In this work, we investigate the inference time of the MobileNet family, EfficientNet V1 and V2 family, VGG models, Resnet family, and InceptionV3 on four edge platforms. Specifically NVIDIA Jetson Nano, Intel Neural Stick, Google Coral USB Dongle, and Google Coral PCIe. Our main contribution is a thorough analysis of the aforementioned models in multiple settings, especially as a function of input size, the presence of the classification head, its size, and the scale of the model. Since throughout the industry, those architectures are mainly utilized as feature extractors we put our main focus on analyzing them as such. We show that Google platforms offer the fastest average inference time, especially for newer models like MobileNet or EfficientNet family, while Intel Neural Stick is the most universal accelerator allowing to run most architectures. These results should provide guidance for engineers in the early stages of AI edge systems development. All of them are accessible at https://bulletprove.com/research/edge_inference_results.csv

本研究分析了 MobileNet，EfficientNet，VGG，Resnet 和 InceptionV3 等多个卷积神经网络在多种设置下的推理时间，结果发现 Google 平台的推理速度最快，特别是对于 MobileNet 或 EfficientNet 等较新的模型；而 Intel Neural Stick 是最通用的加速器，可运行大多数结构。

边缘设备推理性能比较