  The analysis of source code through machine learning techniques is an
increasingly explored research topic aiming at increasing smartness in the
software toolchain to exploit modern architectures in the best possible way. In
the case of low-power, parallel embedded architectures, this means finding the
configuration, for instance in terms of the number of cores, leading to minimum
energy consumption. Depending on the kernel to be executed, the energy optimal
scaling configuration is not trivial. While recent work has focused on
general-purpose systems to learn and predict the best execution target in terms
of the execution time of a snippet of code or kernel (e.g. offload OpenCL
kernel on multicore CPU or GPU), in this work we focus on static compile-time
features to assess if they can be successfully used to predict the minimum
energy configuration on PULP, an ultra-low-power architecture featuring an
on-chip cluster of RISC-V processors. Experiments show that using machine
learning models on the source code to select the best energy scaling
configuration automatically is viable and has the potential to be used in the
context of automatic system configuration for energy minimisation.
