CP$$^{3}$$: Hierarchical Cross-Platform Power/Performance Prediction Using a Transfer Learning Approach

Abstract

Cross-platform power/performance prediction is becoming increasingly important due to the rapid development and variety of software and hardware architectures in an era of heterogeneous multi-core. However, accurate power/performance prediction is faced with an obstacle caused by the large gap between architectures, which is often overcome by laborious and time-consuming fine-grained program profiling on the target platform. To overcome these problems, this paper introduces $$CP^3$$ C P 3 , a hierarchical Cross-platform Power/Performance Prediction framework, which focuses on utilizing architecture differences to migrate built models to target platforms. The core of $$CP^3$$ C P 3 is the three-step hierarchical transfer learning approach, hierarchical division, partial transfer learning, and model fusion, respectively. $$CP^3$$ C P 3 firstly builds a power/performance model on the source platform, then rebuilds it with the reduced training data on the target platform, and finally obtains a cross-platform model. We validate the effectiveness of $$CP^3$$ C P 3 using a group of benchmarks on X86- and ARM-based platforms that use three different types of commonly used processors. Evaluation results show that when applying $$CP^3$$ C P 3 , only 1% of the baseline training data is required to achieve high cross-platform prediction accuracy, with power prediction error being only 0.65%, and performance prediction error being only 4.64%.