TY  - INPR
JF  - ACM Transactions on Software Engineering and Methodology
AV  - public
ID  - discovery10195363
N1  - This version is the author accepted manuscript. For information on re-use, please refer to the publisher?s terms and conditions.
SN  - 1049-331X
A1  - Rajput, Saurabhsingh
A1  - Widmayer, Tim
A1  - Shang, Ziyuan
A1  - Kechagia, Maria
A1  - Sarro, Federica
A1  - Sharma, Tushar
PB  - Association for Computing Machinery (ACM)
Y1  - 2024/07/26/
N2  - 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 (Fine-grained Energy Consumption Meter), 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.
KW  - Energy measurement
KW  -  Green Artificial Intelligence
KW  -  Fine-grained energy measurement
TI  - Enhancing Energy-Awareness in Deep Learning through Fine-Grained Energy Measurement
UR  - https://doi.org/10.1145/3680470
ER  -