CALTEC: Content-Adaptive Linear Tensor Completion For Collaborative Intelligence

Dhondea, Ashiv; Cohen, Robert; Bajić, Ivan V.

doi:10.1109/icip42928.2021.9506372

Cited by 6 publications

(1 citation statement)

References 12 publications

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“…1 SiLRTC and HaLRTC are general methods applicable 1 There are more recent methods based on different modeling of feature tensors, e.g. [5], [6], but these are outside the scope of this comparison.…”

Section: Introductionmentioning

confidence: 99%

Error Resilient Collaborative Intelligence via Low-Rank Tensor Completion

Bragilevsky,

Bajić

2021

Preprint

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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.

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Section: Introductionmentioning

confidence: 99%

Error Resilient Collaborative Intelligence via Low-Rank Tensor Completion

Bragilevsky,

Bajić

2021

Preprint

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SplitFed Resilience to Packet Loss: Where to Split, that is the Question

Shiranthika,

Kafshgari,

Saeedi

et al. 2023

Lecture Notes in Computer Science

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Communication-Oriented Model Fine-Tuning for Packet-Loss Resilient Distributed Inference Under Highly Lossy IoT Networks

et al. 2022

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The distributed inference (DI) framework has gained traction as a technique for real-time applications empowered by cutting-edge deep machine learning (ML) on resource-constrained Internet of things (IoT) devices. In DI, computational tasks are offloaded from the IoT device to the edge server via lossy IoT networks. However, generally, there is a communication system-level trade-off between communication latency and reliability; thus, to provide accurate DI results, a reliable and high-latency communication system is required to be adapted, which results in non-negligible end-to-end latency of the DI. This motivated us to improve the trade-off between the communication latency and accuracy by efforts on ML techniques. Specifically, we have proposed a communication-oriented model tuning (COMtune), which aims to achieve highly accurate DI with low-latency but unreliable communication links. In COMtune, the key idea is to fine-tune the ML model by emulating the effect of unreliable communication links through the application of the dropout technique. This enables the DI system to obtain robustness against unreliable communication links. Our ML experiments revealed that COMtune enables accurate predictions with low latency and under lossy networks.

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CALTEC: Content-Adaptive Linear Tensor Completion For Collaborative Intelligence

Cited by 6 publications

References 12 publications

Error Resilient Collaborative Intelligence via Low-Rank Tensor Completion

Error Resilient Collaborative Intelligence via Low-Rank Tensor Completion

SplitFed Resilience to Packet Loss: Where to Split, that is the Question

Communication-Oriented Model Fine-Tuning for Packet-Loss Resilient Distributed Inference Under Highly Lossy IoT Networks

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