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A layer-wise frequency scaling for a neural processing unitopen access

Authors
Chung, JaehoonKim, HyunMiShin, KyoungseonLyuh, Chun-GiCho, Yong Cheol PeterHan, JinhoKwon, YoungsuGong, Young-HoChung, Sung Woo
Issue Date
10월-2022
Publisher
WILEY
Keywords
dynamic frequency scaling; neural processing unit; power model
Citation
ETRI JOURNAL, v.44, no.5, pp.849 - 858
Indexed
SCIE
SCOPUS
KCI
Journal Title
ETRI JOURNAL
Volume
44
Number
5
Start Page
849
End Page
858
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/145719
DOI
10.4218/etrij.2022-0094
ISSN
1225-6463
Abstract
Dynamic voltage frequency scaling (DVFS) has been widely adopted for run-time power management of various processing units. In the case of neural processing units (NPUs), power management of neural network applications is required to adjust the frequency and voltage every layer to consider the power behavior and performance of each layer. Unfortunately, DVFS is inappropriate for layer-wise run-time power management of NPUs due to the long latency of voltage scaling compared with each layer execution time. Because the frequency scaling is fast enough to keep up with each layer, we propose a layer-wise dynamic frequency scaling (DFS) technique for an NPU. Our proposed DFS exploits the highest frequency under the power limit of an NPU for each layer. To determine the highest allowable frequency, we build a power model to predict the power consumption of an NPU based on a real measurement on the fabricated NPU. Our evaluation results show that our proposed DFS improves frame per second (FPS) by 33% and saves energy by 14% on average, compared with DVFS.
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