FROST: Towards Energy-efficient AI-on-5G Platforms -- A GPU Power
  Capping Evaluation
Ioannis Mavromatis Stefano De Feo Pietro Carnelli Robert J. Piechocki Aftab Khan
The Open Radio Access Network (O-RAN) is a burgeoning market with projected
growth in the upcoming years. RAN has the highest CAPEX impact on the network
and, most importantly, consumes 73% of its total energy. That makes it an ideal
target for optimisation through the integration of Machine Learning (ML).
However, the energy consumption of ML is frequently overlooked in such
ecosystems. Our work addresses this critical aspect by presenting FROST -
Flexible Reconfiguration method with Online System Tuning - a solution for
energy-aware ML pipelines that adhere to O-RAN's specifications and principles.
FROST is capable of profiling the energy consumption of an ML pipeline and
optimising the hardware accordingly, thereby limiting the power draw. Our
findings indicate that FROST can achieve energy savings of up to 26.4% without
compromising the model's accuracy or introducing significant time delays.