Smart City Gnosys
Smart city article details
| Title | Cpu Frequency Scheduling Of Real-Time Applications On Embedded Devices With Temporal Encoding-Based Deep Reinforcement Learning |
|---|---|
| ID_Doc | 16450 |
| Authors | Zhou T.; Lin M. |
| Year | 2023 |
| Published | Journal of Systems Architecture, 142 |
| DOI | http://dx.doi.org/10.1016/j.sysarc.2023.102955 |
| Abstract | Small devices are frequently used in IoT and smart-city applications to perform periodic dedicated tasks with soft deadlines. This work focuses on developing methods to derive efficient power-management methods for periodic tasks on small devices. We first study the limitations of the existing Linux built-in methods used in small devices. We illustrate three typical workload/system patterns that are challenging to manage with Linux's built-in solutions. We develop a reinforcement-learning-based technique with temporal encoding to derive an effective DVFS governor even with the presence of the three system patterns. The derived governor uses only one performance counter, the same as the built-in Linux mechanism, and does not require an explicit task model for the workload. We implemented a prototype system on the Nvidia Jetson Nano Board and experimented with it with six applications, including two self-designed and four benchmark applications. Under different deadline constraints, our approach can quickly derive a DVFS governor that can adapt to performance requirements and outperform the built-in Linux approach in energy saving. On Mibench workloads, with performance slack ranging from 0.04 s to 0.4 s, the proposed method can save 3%–11% more energy compared to Ondemand. AudioReg and FaceReg applications tested have 5%–14% energy-saving improvement. We have open-sourced the implementation of our in-kernel quantized neural network engine. The codebase can be found at: https://github.com/coladog/tinyagent. © 2023 Elsevier B.V. |
| Author Keywords | Energy management for small devices; Reinforcement learning with temporal encoding; Soft-deadline constrained application |
Similar Articles
| Id | Similarity | Authors | Title | Published |
|---|---|---|---|---|
23430  | 0.869 | Sellami B.; Hakiri A.; Yahia S.B.; Berthou P. | Energy-Aware Task Scheduling And Offloading Using Deep Reinforcement Learning In Sdn-Enabled Iot Network | Computer Networks, 210 (2022) |
| 23419 | 0.86 | Hribar J.; Marinescu A.; Chiumento A.; Dasilva L.A. | Energy-Aware Deep Reinforcement Learning Scheduling For Sensors Correlated In Time And Space | IEEE Internet of Things Journal, 9, 9 (2022) |
| 8254 | 0.859 | Yu K.; Yang Y.; Xiao H.; Chen J. | An Improved Dvfs Algorithm For Energy-Efficient Real-Time Task Scheduling | Proceedings - 2020 IEEE 22nd International Conference on High Performance Computing and Communications, IEEE 18th International Conference on Smart City and IEEE 6th International Conference on Data Science and Systems, HPCC-SmartCity-DSS 2020 (2020) |