TY - GEN
T1 - Energy Efficiency and Timeliness in Model Training for Internet-of-Things Applications
T2 - 6th ACM/IEEE International Conference on Internet of Things Design and Implementation, IoTDI 2021
AU - Mei, Chih Shuo
AU - Wang, Chao
N1 - Publisher Copyright:
© 2021 ACM.
PY - 2021/5/18
Y1 - 2021/5/18
N2 - Neural network model training is indispensable for domain-specific Artificial Intelligent Internet-of-Things (AIoT) applications. Typically, a GPU graphics card may take several hundreds watts in average during model training, while an embedded GPU device may take only couple watts for the same purpose at the cost of a longer training time. In this paper, we report our empirical study on the model training using NVIDIA RTX 2080 Ti graphics card and NVIDIA Jetson Nano embedded device. We show that, surprisingly, while the training time using the Jetson Nano is 30 times slower than that using the graphics card, the total energy consumption by Jetson Nano is actually only half. The result suggests that when the response time is less critical, one may choose to do model training on GPU embedded devices instead.
AB - Neural network model training is indispensable for domain-specific Artificial Intelligent Internet-of-Things (AIoT) applications. Typically, a GPU graphics card may take several hundreds watts in average during model training, while an embedded GPU device may take only couple watts for the same purpose at the cost of a longer training time. In this paper, we report our empirical study on the model training using NVIDIA RTX 2080 Ti graphics card and NVIDIA Jetson Nano embedded device. We show that, surprisingly, while the training time using the Jetson Nano is 30 times slower than that using the graphics card, the total energy consumption by Jetson Nano is actually only half. The result suggests that when the response time is less critical, one may choose to do model training on GPU embedded devices instead.
KW - Deep Learning
KW - Embedded Systems
KW - Energy Efficiency
UR - http://www.scopus.com/inward/record.url?scp=85107232306&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85107232306&partnerID=8YFLogxK
U2 - 10.1145/3450268.3453507
DO - 10.1145/3450268.3453507
M3 - Conference contribution
AN - SCOPUS:85107232306
T3 - IoTDI 2021 - Proceedings of the 2021 International Conference on Internet-of-Things Design and Implementation
SP - 253
EP - 254
BT - IoTDI 2021 - Proceedings of the 2021 International Conference on Internet-of-Things Design and Implementation
PB - Association for Computing Machinery, Inc
Y2 - 18 May 2021 through 21 May 2021
ER -
