Machine Learning-Based Processor Adaptability Targeting Energy, Performance, and Reliability

Abstract

Adaptive processors can dynamically change their hardware configuration by tuning several knobs that optimize a given metric, according to the current application. However, the complexity of choosing the best setup at runtime increases exponentially as more adaptive resources become available. Therefore, we propose a polymorphic VLIW processor coupled to a machine learning-based decision mechanism that quickly and accurately delivers the best trade-off in terms of energy, performance, and reliability. The proposed system predicts the best processor configuration in 97.37% of the test cases and achieves an efficiency that is close to an oracle (more than 93.30% on all benchmarks).