Error Resilient Collaborative Intelligence via Low-Rank Tensor Completion

TL;DR

This paper investigates the use of low-rank tensor completion techniques to recover missing data in deep feature tensors transmitted between edge devices and the cloud, enhancing collaborative AI robustness.

Contribution

It evaluates four low-rank tensor completion methods for improving data recovery in collaborative AI, considering both sparse and non-sparse tensors under various complexity constraints.

Findings

Low-rank tensor completion effectively recovers missing deep features.

Sparse tensors like VGG16 benefit from specific completion methods.

Non-sparse tensors like ResNet34 also show improved recovery performance.

Abstract

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.