Enabling Resource-Efficient AIoT System With Cross-Level Optimization : A Survey

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

6 Scopus Citations
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Author(s)

  • Sicong Liu
  • Bin Guo
  • Cheng Fang
  • Ziqi Wang
  • Shiyan Luo
  • Zhiwen Yu

Related Research Unit(s)

Detail(s)

Original languageEnglish
Pages (from-to)389-427
Number of pages39
Journal / PublicationIEEE Communications Surveys and Tutorials
Volume26
Issue number1
Online published27 Sept 2023
Publication statusPublished - 2024

Link(s)

Abstract

The emerging field of artificial intelligence of things (AIoT, AI+IoT) is driven by the widespread use of intelligent infrastructures and the impressive success of deep learning (DL). With the deployment of DL on various intelligent infrastructures featuring rich sensors and weak DL computing capabilities, a diverse range of AIoT applications has become possible. However, DL models are notoriously resource-intensive. Existing research strives to realize near-/realtime inference of AIoT live data and low-cost training using AIoT datasets on resource-scare infrastructures. Accordingly, the accuracy and responsiveness of DL models are bounded by resource availability. To this end, the algorithm-system co-design that jointly optimizes the resource-friendly DL models and model-adaptive system scheduling improves the runtime resource availability and thus pushes the performance boundary set by the standalone level. Unlike previous surveys on resource-friendly DL models or hand-crafted DL compilers/frameworks with partially fine-tuned components, this survey aims to provide a broader optimization space for more free resource-performance tradeoffs. The cross-level optimization landscape involves various granularity, including the DL model, computation graph, operator, memory schedule, and hardware instructor in both on-device and distributed paradigms. Furthermore, due to the dynamic nature of AIoT context, which includes heterogeneous hardware, agnostic sensing data, varying userspecified performance demands, and resource constraints, this survey explores the context-aware inter-/intra-device controllers for automatic cross-level adaptation. Additionally, we identify some potential directions for resource-efficient AIoT systems. By consolidating problems and techniques scattered over diverse levels, we aim to help readers understand their connections and stimulate further discussions. © 2023 IEEE.

Research Area(s)

  • Artificial intelligence, Computational modeling, cross-level optimization, DL inference and training tasks, Internet of Things, Optimization, Resource-efficient AIoT system, Sensors, Surveys, Training

Citation Format(s)

Enabling Resource-Efficient AIoT System With Cross-Level Optimization: A Survey. / Liu, Sicong; Guo, Bin; Fang, Cheng et al.
In: IEEE Communications Surveys and Tutorials, Vol. 26, No. 1, 2024, p. 389-427.

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

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