LAUSR

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.