Enhancing Energy-Awareness in Deep Learning through Fine-Grained Energy Measurement

Abstract

With the increasing usage, scale, and complexity of Deep Learning (DL) models, their rapidly growing energy consumption has become a critical concern. Promoting green development and energy awareness at different granularities is the need of the hour to limit carbon emissions of dl systems. However, the lack of standard and repeatable tools to accurately measure and optimize energy consumption at fine granularity ( e.g ., at the API level) hinders progress in this area. This paper introduces FECoM ( F ine-grained E nergy Co nsumption M eter ), a framework for fine-grained DL energy consumption measurement. FECoM enables researchers and developers to profile DL APIS from energy perspective. FECoM addresses the challenges of fine-grained energy measurement using static instrumentation while considering factors such as computational load and temperature stability. We assess FECoM’s capability for fine-grained energy measurement for one of the most popular open-source DL frameworks, namely TENSORFLOW. Using FECoM, we also investigate the impact of parameter size and execution time on energy consumption, enriching our understanding of TENSORFLOW APIS’ energy profiles. Furthermore, we elaborate on the considerations and challenges while designing and implementing a fine-grained energy measurement tool. This work will facilitate further advances in dl energy measurement and the development of energy-aware practices for DL systems.