TY - GEN
T1 - Implementation of Edge-cloud Cooperative CNN Inference on an IoT Platform
AU - Wang, Yuan
AU - Shibamura, Hidetomo
AU - Ng, Kuan Yi
AU - Inoue, Koji
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Since the Internet of Things (IoT) has become more widely used in various industrial situations, Artificial Intelligence (AI) programs, particularly Convolutional Neural Network (CNN) applications, are projected to be implemented on edge devices to meet high-accuracy and huge industry computing needs. Offloading computing-intensive workloads to the cloud is a promising solution for compact energy-constrained edge devices, but it tends to incur significant costs in total execution latency. For flexible and fine-grained offloading, this paper aims to design and implement an edge-cloud cooperative CNN inference framework on an IoT platform by targeting TensorFlow Lite. We have confirmed the implementation's feasibility and accuracy through the verification of implementing LeNet, AlexNet, and VGGNet. Intending to perform high-performance edge-cloud AI executions on the presented IoT platform, we evaluate the performance overhead (total execution latency) of the provided implementation and identify the current bottlenecks of the target platform for enhancing it.
AB - Since the Internet of Things (IoT) has become more widely used in various industrial situations, Artificial Intelligence (AI) programs, particularly Convolutional Neural Network (CNN) applications, are projected to be implemented on edge devices to meet high-accuracy and huge industry computing needs. Offloading computing-intensive workloads to the cloud is a promising solution for compact energy-constrained edge devices, but it tends to incur significant costs in total execution latency. For flexible and fine-grained offloading, this paper aims to design and implement an edge-cloud cooperative CNN inference framework on an IoT platform by targeting TensorFlow Lite. We have confirmed the implementation's feasibility and accuracy through the verification of implementing LeNet, AlexNet, and VGGNet. Intending to perform high-performance edge-cloud AI executions on the presented IoT platform, we evaluate the performance overhead (total execution latency) of the provided implementation and identify the current bottlenecks of the target platform for enhancing it.
UR - https://www.scopus.com/pages/publications/85147436538
UR - https://www.scopus.com/inward/citedby.url?scp=85147436538&partnerID=8YFLogxK
U2 - 10.1109/MCSoC57363.2022.00060
DO - 10.1109/MCSoC57363.2022.00060
M3 - Conference contribution
AN - SCOPUS:85147436538
T3 - Proceedings - 2022 IEEE 15th International Symposium on Embedded Multicore/Many-Core Systems-on-Chip, MCSoC 2022
SP - 337
EP - 344
BT - Proceedings - 2022 IEEE 15th International Symposium on Embedded Multicore/Many-Core Systems-on-Chip, MCSoC 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 15th IEEE International Symposium on Embedded Multicore/Many-Core Systems-on-Chip, MCSoC 2022
Y2 - 19 December 2022 through 22 December 2022
ER -