TY - GEN
T1 - EnergySense
T2 - 9th IEEE Smart World Congress, SWC 2023
AU - Ren, Jiaju
AU - Yu, Zhiwen
AU - Xing, Tao
AU - Cui, Helei
AU - Chen, Yaxing
AU - Guo, Bin
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Energy efficiency is the key requirement to maximize the lifetime of ubiquitous sensors, such as mobile phones, wearable devices and resource-constrained sensors. Such sensors offer new possibilities for providing AI-based applications and services in low-power, low-cost local processing with real-time feedback. However, they have difficulty handling computationally intensive feature extraction for deep learning because they are typically powered by batteries with a finite lifetime. To address this challenge, we propose an energy-efficient analysis framework. 1) A response-adaptive energy model is proposed to evaluate the global consumption of a ubiquitous computing system in low-power operation mode. 2) We also report the design and implementation of EnergySense, which is an energy-efficient scheduling framework, and further optimize the energy efficiency of the execution of DNN-based tasks. Extensive experiments verify the framework's effectiveness in reducing energy consumption compared to the baseline method. With up to 10 scheduled sensors, the energy consumption for DNN computing is reduced by 67.7% compared to the case of a single device. As a result, Energy-Sense provides a longer life cycle of the feature map extraction and improves the computing power of low-power ubiquitous sensors.
AB - Energy efficiency is the key requirement to maximize the lifetime of ubiquitous sensors, such as mobile phones, wearable devices and resource-constrained sensors. Such sensors offer new possibilities for providing AI-based applications and services in low-power, low-cost local processing with real-time feedback. However, they have difficulty handling computationally intensive feature extraction for deep learning because they are typically powered by batteries with a finite lifetime. To address this challenge, we propose an energy-efficient analysis framework. 1) A response-adaptive energy model is proposed to evaluate the global consumption of a ubiquitous computing system in low-power operation mode. 2) We also report the design and implementation of EnergySense, which is an energy-efficient scheduling framework, and further optimize the energy efficiency of the execution of DNN-based tasks. Extensive experiments verify the framework's effectiveness in reducing energy consumption compared to the baseline method. With up to 10 scheduled sensors, the energy consumption for DNN computing is reduced by 67.7% compared to the case of a single device. As a result, Energy-Sense provides a longer life cycle of the feature map extraction and improves the computing power of low-power ubiquitous sensors.
KW - DNN inference
KW - energy-efficient framework
KW - low power
KW - ubiquitous computing
UR - http://www.scopus.com/inward/record.url?scp=85187378790&partnerID=8YFLogxK
U2 - 10.1109/SWC57546.2023.10449264
DO - 10.1109/SWC57546.2023.10449264
M3 - 会议稿件
AN - SCOPUS:85187378790
T3 - Proceedings - 2023 IEEE SmartWorld, Ubiquitous Intelligence and Computing, Autonomous and Trusted Vehicles, Scalable Computing and Communications, Digital Twin, Privacy Computing and Data Security, Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PCDS/Metaverse 2023
BT - Proceedings - 2023 IEEE SmartWorld, Ubiquitous Intelligence and Computing, Autonomous and Trusted Vehicles, Scalable Computing and Communications, Digital Twin, Privacy Computing and Data Security, Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PCDS/Metaverse 2023
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 28 August 2023 through 31 August 2023
ER -
