Dynamic microarchitectural adaptation using machine learning

Christophe Dubach, Timothy M. Jones, Edwin V. Bonilla

ACM Transactions on Architecture and Code Optimization 2013 article cited by 16

Source Open-access PDF More in Engineering & Materials

importance 5/10 solved Parallel Computing and Optimization Techniques processor design stage: research

Problem

How to dynamically determine the best microprocessor configuration at runtime for any program or program phase, to improve energy and performance efficiency.

Why it matters

Power-efficient, adaptively configured microprocessors reduce energy cost across all computing.

What the AI did

A machine-learning predictive model monitors application phase behaviour at runtime and controls microarchitectural adaptivity, selecting configurations automatically.

Results

On SPEC 2000, doubles energy/performance efficiency versus the best static configuration, achieving 74% of the ideal improvement.

Impact

Peer-reviewed demonstration of doubled energy/performance efficiency on SPEC 2000 benchmarks.

Record

Venue	ACM Transactions on Architecture and Code Optimization
Published	2013
Work type	article
Authors	Christophe Dubach, Timothy M. Jones, Edwin V. Bonilla
Citations	16 (OpenAlex, at import time)
References	54
Model or method	not stated in source
Organization	not stated in source
Primary source	https://doi.org/10.1145/2541228.2541238
Open access	bronze
Keywords	machine learning, microarchitecture, energy efficiency, runtime adaptation, predictive model

Importance rating

5 / 10

real but narrower advance

Energy-efficient microprocessors matter broadly to computing, but abstract gives no scale; benefit is a doubled efficiency on SPEC 2000 benchmarks only.

Basis: inferred. Judged from the source text, which gives no explicit scale. Stated scale: not stated in source. Method and limits.

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