eprintid: 10195363
rev_number: 7
eprint_status: archive
userid: 699
dir: disk0/10/19/53/63
datestamp: 2024-08-05 12:08:13
lastmod: 2024-08-05 12:08:13
status_changed: 2024-08-05 12:08:13
type: article
metadata_visibility: show
sword_depositor: 699
creators_name: Rajput, Saurabhsingh
creators_name: Widmayer, Tim
creators_name: Shang, Ziyuan
creators_name: Kechagia, Maria
creators_name: Sarro, Federica
creators_name: Sharma, Tushar
title: Enhancing Energy-Awareness in Deep Learning through Fine-Grained Energy Measurement
ispublished: inpress
divisions: UCL
divisions: B04
divisions: F48
keywords: Energy measurement, Green Artificial Intelligence, Fine-grained energy measurement
note: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
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 (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.
date: 2024-07-26
date_type: published
publisher: Association for Computing Machinery (ACM)
official_url: https://doi.org/10.1145/3680470
oa_status: green
full_text_type: other
language: eng
primo: open
primo_central: open_green
verified: verified_manual
elements_id: 2302382
doi: 10.1145/3680470
lyricists_name: Kechagia, Maria
lyricists_name: Sarro, Federica
lyricists_id: MKECH60
lyricists_id: FSSAR72
actors_name: Bracey, Alan
actors_id: ABBRA90
actors_role: owner
full_text_status: public
publication: ACM Transactions on Software Engineering and Methodology
issn: 1049-331X
citation:        Rajput, Saurabhsingh;    Widmayer, Tim;    Shang, Ziyuan;    Kechagia, Maria;    Sarro, Federica;    Sharma, Tushar;      (2024)    Enhancing Energy-Awareness in Deep Learning through Fine-Grained Energy Measurement.                   ACM Transactions on Software Engineering and Methodology        10.1145/3680470 <https://doi.org/10.1145/3680470>.    (In press).    Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/10195363/1/3680470%20%281%29.pdf