Smart City Gnosys
Smart city article details
| Title | Hierarchical Scheduling Of Hybrid Dnn Tasks In Embedded Real-Time Systems |
|---|---|
| ID_Doc | 29022 |
| Authors | Feng J.; Zhu K.; Zhang T. |
| Year | 2023 |
| Published | Proceedings of the International Conference on Parallel and Distributed Systems - ICPADS |
| DOI | http://dx.doi.org/10.1109/ICPADS60453.2023.00389 |
| Abstract | With the widespread application of deep learning (DL) technology in the modern Internet of Things (IoT) areas such as autonomous driving, smart cities and homes, embedded real-time systems are increasingly used at the edge of the network to complete various hybrid DNN tasks. Although embedded real-time systems are equipped with heterogeneous CPU and GPU cores to reduce the response time of inference jobs, the computing resources of heterogeneous devices are not fully utilized, and there is still plenty of room for schedulability to be improved. In this paper, we propose a layer-based hybrid deep neural network (DNN) tasks scheduling algorithm in embedded real-time systems (LHTS) that maps DNN layers to CPU and GPU devices and regulates their start time to avoid confliction. We evaluate LHTS through extensive simulations. The experimental results show that LHTS can achieve more sufficient use of heterogeneous CPU and GPU resources in embedded real-time systems, reduce the worst-case execution time and enhance the schedulability performance of hybrid DNN tasks. © 2023 IEEE. |
| Author Keywords | deep neural network; optimization; real-time system; resource allocation; task scheduling |
Similar Articles
| Id | Similarity | Authors | Title | Published |
|---|---|---|---|---|
6307  | 0.872 | Zhou L.; Samavatian M.H.; Bacha A.; Majumdar S.; Teodorescu R. | Adaptive Parallel Execution Of Deep Neural Networks On Heterogeneous Edge Devices | Proceedings of the 4th ACM/IEEE Symposium on Edge Computing, SEC 2019 (2019) |
| 32524 | 0.863 | Vigenesh M.; Katyal A.; Hemalatha S.; Ahluwalia G.; Kukreja M.; Mathurkar P. | Intelligent Resource Scheduling For Edge-Integrated Iot Using Deep Learning | 2024 IEEE 4th International Conference on ICT in Business Industry and Government, ICTBIG 2024 (2024) |
| 14476 | 0.858 | Lilhore U.K.; Simaiya S.; Sharma Y.K.; Rai A.K.; Padmaja S.M.; Nabilal K.V.; Kumar V.; Alroobaea R.; Alsufyani H. | Cloud-Edge Hybrid Deep Learning Framework For Scalable Iot Resource Optimization | Journal of Cloud Computing, 14, 1 (2025) |