Error Resilient Collaborative Intelligence via Low-Rank Tensor Completion
This work addresses communication errors in edge-cloud AI systems, but it is incremental as it applies existing tensor completion methods to a specific domain.
The study tackled the problem of missing data in deep feature tensors transmitted from edge to cloud in collaborative intelligence systems by evaluating four low-rank tensor completion methods, finding that these methods effectively recover missing data in both sparse and non-sparse tensors from models like VGG16 and ResNet34.
In the race to bring Artificial Intelligence (AI) to the edge, collaborative intelligence has emerged as a promising way to lighten the computation load on edge devices that run applications based on Deep Neural Networks (DNNs). Typically, a deep model is split at a certain layer into edge and cloud sub-models. The deep feature tensor produced by the edge sub-model is transmitted to the cloud, where the remaining computationally intensive workload is performed by the cloud sub-model. The communication channel between the edge and cloud is imperfect, which will result in missing data in the deep feature tensor received at the cloud side. In this study, we examine the effectiveness of four low-rank tensor completion methods in recovering missing data in the deep feature tensor. We consider both sparse tensors, such as those produced by the VGG16 model, as well as non-sparse tensors, such as those produced by ResNet34 model. We study tensor completion effectiveness in both conplexity-constrained and unconstrained scenario.