Machine Learning-Based Processor Adaptability Targeting Energy, Performance, and Reliability
2019 IEEE Computer Society Annual Symposium on VLSI (ISVLSI)(2019)
摘要
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).
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关键词
Machine learning,Adaptive processor,Energy consumption,Fault tolerance,Runtime optimization
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