FROST: Towards Energy-efficient AI-on-5G Platforms -- A GPU Power Capping Evaluation
This work addresses energy efficiency for AI applications in 5G networks, offering a domain-specific optimization that is incremental in nature.
The paper tackles the problem of high energy consumption in Open Radio Access Networks (O-RAN), which accounts for 73% of network energy, by proposing FROST, an energy-aware ML pipeline solution that achieves up to 26.4% energy savings without compromising model accuracy or causing significant delays.
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.