  Emerging applications such as Deep Learning are often data-driven, thus
traditional approaches based on auto-tuners are not performance effective
across the wide range of inputs used in practice. In the present paper, we
start an investigation of predictive models based on machine learning
techniques in order to optimize Convolution Neural Networks (CNNs). As a
use-case, we focus on the ARM Compute Library which provides three different
implementations of the convolution operator at different numeric precision.
Starting from a collation of benchmarks, we build and validate models learned
by Decision Tree and naive Bayesian classifier. Preliminary experiments on
Midgard-based ARM Mali GPU show that our predictive model outperforms all the
convolution operators manually selected by the library.
