Energy-Efficient Convolution Module With Flexible Bit-Adjustment Method and ADC Multiplier Architecture for Industrial IoT

Li, Tao; Ma, Yi; Yoshikawa, Ko; Nomura, Osamu; Endoh, Tetsuo

doi:10.1109/tii.2021.3106242

Cited by 3 publications

(1 citation statement)

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“…Concepts such as smart medical healthcare [2,3], smart buildings [4,5], smart grid [6,7], and smart transportation [8,9] have begun to be proposed. e Industrial Internet of ings (IIoT) means the use of IoT technology in the industry, which makes industrial manufacturing intelligent [10,11]. e development of IoT technology will undoubtedly make life smarter in the future.…”

Section: Introductionmentioning

confidence: 99%

Construction of a Smart Supply Chain for Sand Factory Using the Edge-Computing-Based Deep Learning Algorithm

Zhang

Ban

et al. 2022

Scientific Programming

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The diminishing natural sand has facilitated the booming of the sand manufacturing industry, and intelligent management of sand factories, in a time- and cost-efficient way, has become a growing tendency for the future. A role has been played in achieving intelligent management by constructing a smart supply chain. However, the smart sand factories are hardly involved in previously reported studies, which is inconsistent with related studies on smart factories and the Industrial Internet of Things (IIoT). In this paper, a smart supply chain management system (SSCMS) is constructed to realize the intelligence and automatization of the management of sand factories, using edge-computing and deep learning techniques. Along the supply chain, the deep learning model is used to realize the automatic identification of sand, avoiding the disadvantages of human identification, while improving the quality of sand factory operations. In order to relieve the pressure of network bandwidth, reduce system delay, and improve system operation efficiency, we use edge-computing technology to process data at the edge. To verify the performance of the constructed system, a sand factory simulation platform is established. Experiments show that the most critical indicator in the system, the accuracy rate of sand type identification, is above 98%, and the sand type identification time is only 0.022 s. In general, compared with traditional supply chain management, the constructed smart supply chain improves the quality and efficiency of sand factory operations, and all indicators of the designed system have achieved satisfactory results.

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Section: Introductionmentioning

confidence: 99%

Construction of a Smart Supply Chain for Sand Factory Using the Edge-Computing-Based Deep Learning Algorithm

Zhang

Ban

et al. 2022

Scientific Programming

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From Algorithm to Module: Adaptive and Energy-Efficient Quantization Method for Edge Artificial Intelligence in IoT Society

Endoh

2023

IEEE Trans. Ind. Inf.

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Cloud-Fog Automation: Vision, Enabling Technologies, and Future Research Directions

Jin

Kua

et al. 2024

IEEE Trans. Ind. Inf.

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Energy-Efficient Convolution Module With Flexible Bit-Adjustment Method and ADC Multiplier Architecture for Industrial IoT

Cited by 3 publications

References 25 publications

Construction of a Smart Supply Chain for Sand Factory Using the Edge-Computing-Based Deep Learning Algorithm

Construction of a Smart Supply Chain for Sand Factory Using the Edge-Computing-Based Deep Learning Algorithm

From Algorithm to Module: Adaptive and Energy-Efficient Quantization Method for Edge Artificial Intelligence in IoT Society

Cloud-Fog Automation: Vision, Enabling Technologies, and Future Research Directions

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