Conference proceeding
Work Load Scheduling For Multi Core Systems With Under-Provisioned Power Delivery
PROCEEDINGS OF THE GREAT LAKES SYMPOSIUM ON VLSI 2017 (GLSVLSI' 17), v 127756, pp 387-392
01 Jan 2017
Abstract
An energy efficient power delivery method for multi-core systems with under-provisioned on-chip voltage regulators has been proposed in literature. The power delivery network is reconfigurable at run-time to meet the varying current demands of the cores exceeding the maximum output current rating of the voltage regulators. In this paper, a real-time workload scheduling heuristic is developed that assigns the tasks to the cores such that the total load current consumption of the cores is always less than the total current capability of the under-provisioned on-chip voltage regulators. In addition, the energy-efficient scheduling of the tasks on to the cores ensures that the reconfiguration of the power delivery network is minimized. The heuristic includes DVFS management based on the unique constraints of the under provisioned voltage regulators. The work load scheduler is evaluated on homogeneous and heterogeneous multi-core platforms based on the Exynos 5410 big.LITTLE architecture. The proposed workload scheduler along with the run time voltage regulator clustering algorithm proposed in the literature provides a robust cross-layer power management technique for under provisioned on-chip power delivery.
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Details
- Title
- Work Load Scheduling For Multi Core Systems With Under-Provisioned Power Delivery
- Creators
- Divya Pathak - Drexel UniversityHouman Homayoun - George Mason UniversityIoannis Savidis - Drexel UniversityACM
- Publication Details
- PROCEEDINGS OF THE GREAT LAKES SYMPOSIUM ON VLSI 2017 (GLSVLSI' 17), v 127756, pp 387-392
- Conference
- GREAT LAKES SYMPOSIUM ON VLSI 2017 (GLSVLSI' 17)
- Publisher
- Assoc Computing Machinery
- Number of pages
- 6
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Electrical and Computer Engineering
- Web of Science ID
- WOS:000568262800069
- Scopus ID
- 2-s2.0-85021229180
- Other Identifier
- 991019168425804721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Computer Science, Hardware & Architecture
- Computer Science, Information Systems
- Computer Science, Theory & Methods
- Engineering, Electrical & Electronic