Enabling AI Quality Control via Feature Hierarchical Edge Inference
- URL: http://arxiv.org/abs/2211.07860v1
- Date: Tue, 15 Nov 2022 02:54:23 GMT
- Title: Enabling AI Quality Control via Feature Hierarchical Edge Inference
- Authors: Jinhyuk Choi, Seong-Lyun Kim, Seung-Woo Ko
- Abstract summary: This work proposes a feature hierarchical EI (FHEI) comprising feature network and inference network deployed at an edge server and corresponding mobile.
A higher scale feature requires more computation and communication loads while it provides a better AI quality.
It is verified by extensive simulations that the proposed joint communication-and-computation control on FHEI architecture always outperforms several benchmarks.
- Score: 6.490724361345847
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: With the rise of edge computing, various AI services are expected to be
available at a mobile side through the inference based on deep neural network
(DNN) operated at the network edge, called edge inference (EI). On the other
hand, the resulting AI quality (e.g., mean average precision in objective
detection) has been regarded as a given factor, and AI quality control has yet
to be explored despite its importance in addressing the diverse demands of
different users. This work aims at tackling the issue by proposing a feature
hierarchical EI (FHEI), comprising feature network and inference network
deployed at an edge server and corresponding mobile, respectively.
Specifically, feature network is designed based on feature hierarchy, a
one-directional feature dependency with a different scale. A higher scale
feature requires more computation and communication loads while it provides a
better AI quality. The tradeoff enables FHEI to control AI quality gradually
w.r.t. communication and computation loads, leading to deriving a
near-to-optimal solution to maximize multi-user AI quality under the
constraints of uplink \& downlink transmissions and edge server and mobile
computation capabilities. It is verified by extensive simulations that the
proposed joint communication-and-computation control on FHEI architecture
always outperforms several benchmarks by differentiating each user's AI quality
depending on the communication and computation conditions.
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