Automatic compiler optimization on embedded software through k-means clustering
Sprache des Vortragstitels:
Englisch
Original Tagungtitel:
Workshop on Machine Learning for CAD (MLCAD)
Sprache des Tagungstitel:
Englisch
Original Kurzfassung:
Generating instead of implementing variable design platforms is
becoming increasingly popular in the development of System on
Chips (SoCs). This shift also poses the challenge of rapid compiler
optimization that adapts to each newly generated platform.
In this paper, we evaluate the impact of 104 compiler flags on
memory usage and core execution time against standard optimization levels. Each flag has a different influence on these costs, which
is difficult to predict. In this work, we apply cost estimation methods to predict the impact of each flag on the generated core using
unsupervised Machine Learning (ML), in the form of k-means clustering. The key strengths of the approach are the low need for
data, the adaptability to new cores, and the ease of use. This helps
the designer to understand the impact of flags on related applications, showing which combination is optimizing the most. As
a result, we can obtain 20,93% optimization on the software size,
3,10% on the performance, and 1,75% on their trade-off beyond the
-O3 optimization.
Forschungs-