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

Conference paper (2019)

Authors

A.L. Sartor Carnegie Mellon University

Pedro Henrique Exenberger Becker Universidade Federal do Rio Grande do Sul

J.S.S.M. Wong Computer Engineering -

Radu Marculescu Carnegie Mellon University

Antonio Carlos Schneider Beck Universidade Federal do Rio Grande do Sul

Research Group

Computer Engineering () (TU Delft)

Machine learning Fault tolerance Energy consumption Adaptive processor Runtime optimization

To reference this document use:

http://resolver.tudelft.nl/uuid:7faf83f9-e8ad-42e5-ab47-b8ccb9069124

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Published Date

01-07-2019

Language

English

Reuse Rights

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Quantum & Computer Engineering

Research Group

Computer Engineering

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).

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