Edge offloading for deep neural networks (DNNs) can be adaptive to the input's complexity by using early-exit DNNs. These DNNs have side branches throughout their architecture, allowing the inference to end earlier in the edge. The branches estimate the accuracy for a given input. If this estimated accuracy reaches a threshold, the inference ends on the edge. Otherwise, the edge offloads the inference to the cloud to process the remaining DNN layers. However, DNNs for image classification deals with distorted images, which negatively impact the branches' estimated accuracy. Consequently, the edge offloads more inferences to the cloud. This work introduces expert side branches trained on a particular distortion type to improve robustness against image distortion. The edge detects the distortion type and selects appropriate expert branches to perform the inference. This approach increases the estimated accuracy on the edge, improving the offloading decisions. We validate our proposal in a realistic scenario, in which the edge offloads DNN inference to Amazon EC2 instances.

翻译：深海神经网络(DNN) 的边缘卸载可以通过使用早期排出 DNNs 来适应输入的复杂性。 这些 DNNs 在整个结构结构中都有侧形分支, 允许在边缘提前结束推论。 分支估计了给定输入的准确性。 如果这一估计准确性达到临界值, 则推论在边缘结束。 否则, 边缘卸载云层的推论来处理其余的 DNNT 层。 然而, 图像分类的DNNs 处理的是扭曲的图像, 这会对分支的估计准确性产生消极影响。 因此, 边缘卸载对云层的推论更多。 这项工作引入了专家侧形分支对特定扭曲类型进行的培训, 以提高对图像扭曲的稳健性。 边缘检测了扭曲类型, 并选择了适当的专家分支来进行推论。 这种方法提高了边缘的估计精度, 改进了卸决定。 我们验证了我们的提案, 在一种现实的情景下, 边端卸 DNNNE 推论到亚马 EC2 实例 。