Journal article
ElasticCore: A Dynamic Heterogeneous Platform With Joint Core and Voltage/Frequency Scaling
IEEE transactions on very large scale integration (VLSI) systems, v 26(2), pp 249-261
Feb 2018
Abstract
Heterogeneous architectures have emerged as a promising solution to address the dark silicon challenge by providing customized cores for each running application. To harness the power of heterogeneity, a critical challenge is simultaneously fine-tuning several parameters at the application, architecture, system, as well as circuit levels for heterogeneous architectures that improve the energy-efficiency envelope. To address this challenge, an ElasticCore platform is described where core resources along with the operating voltage and frequency settings are scaled to match the application behavior at run-time. A quantile linear regression model for power and performance prediction is used to guide the adaptation of the core resources, along with the operating voltage and frequency, to improve the energy efficiency. In addition, the dynamically scalable partitions of the ElasticCore are powered with multiple on-chip voltage regulators with high-power conversion efficiency that are able to realize fast dynamic voltage/frequency scaling. The results indicate that ElasticCore predicts application power and performance behavior with a small error at run-time across all studied benchmarks and achieves, on average close to 93% energy efficiency, as compared to an architecture with the Oracle power and performance predictor.
Metrics
Details
- Title
- ElasticCore: A Dynamic Heterogeneous Platform With Joint Core and Voltage/Frequency Scaling
- Creators
- Mohammad Khavari Tavana - George Mason University, Fairfax, VA, USAMohammad Hossein Hajkazemi - George Mason University, Fairfax, VA, USADivya Pathak - Department of Electrical and Computer Engineering, Drexel University, Philadelphia, PA, USAIoannis Savidis - Department of Electrical and Computer Engineering, Drexel University, Philadelphia, PA, USAHouman Homayoun - Department of Electrical and Computer Engineering, George Mason University, Fairfax, VA, USA
- Publication Details
- IEEE transactions on very large scale integration (VLSI) systems, v 26(2), pp 249-261
- Publisher
- IEEE
- Grant note
- CNS 1526913 / National Science Foundation (10.13039/100000001)
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Electrical and Computer Engineering
- Web of Science ID
- WOS:000423464000004
- Scopus ID
- 2-s2.0-85035122843
- Other Identifier
- 991014878596204721
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
- Engineering, Electrical & Electronic