The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.
GPU Energy Consumption Optimization With a Global-Based Neural Network Method
Open accessAbstract
With the widespread use of smart technologies, graphics processing unit (GPU) power-optimization issues are becoming increasingly important. Many researchers have tried to use dynamic voltage and frequency scaling (DVFS) technology to optimize a GPU's internal energy consumption. However, DVFS energy management often has difficulty balancing GPU performance and energy efficiency. This paper aims to implement a DVFS energy management strategy. We constructed a new type of neural network to a GPU-based energy management scheme, implemented the global-based DVFS model, and explored its implementation details. Using a master-slave model, we built a global energy control solution strategy. This strategy performs global collaborative DVFS adjustments on the GPU's energy consumption module based on task characteristics. Through the software construction and implementation of the global-based DVFS model, we proved that the strategy improves the GPU performance while improving the GPU's energy efficiency. We conducted performance and energy tests on three GPUs on the Tesla, Fermi, and Kepler platforms. The experiments showed that this strategy improved the performance and power consumption of GPUs based on each of the platforms.
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“standard CPUs consume generally up to 85 watts and standard GPUs consume between 200 watts to 500 watts [11] [12].”
From A Comprehensive Survey on TinyML (2023) · cites this paper as [12]
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Cites
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Self-citation
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Krzywaniak, A., Czarnul, P., & Proficz, J. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-08751-6_48 · Cited by 25
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Ang, L.-M., & Seng, K. P. (2021) · Electronics · doi:10.3390/electronics10080952 · Cited by 24
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Evaluating Energy Efficiency of GPUs using Machine Learning Benchmarks
Foster, B., Taneja, S., Manzano, J., & BARKER, K. J. (2023) · IEEE International Symposium on Parallel & Distributed Processing, Workshops and Phd Forum · doi:10.1109/ipdpsw59300.2023.00019 · Cited by 9
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Exploiting Non-conventional DVFS on GPUs: Application to Deep Learning
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GPGPU performance estimation for frequency scaling using cross-benchmarking
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Energy-aware Task Scheduling with Deadline Constraint in DVFS-enabled Heterogeneous ClustersPreprint
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Chung, H., Im, Y., Park, J., Lee, T. K. J.-M., Kim, T. Y., Kim, H., & Lee, D. (2025) · IEEE Access · doi:10.1109/access.2025.3569803 · Cited by 2
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Gül, F. (2026) · Journal of King Saud University - Computer and Information Sciences · doi:10.1007/s44443-026-00900-6 · Cited by 1
Mohamed, M. F., & Hamed, A. (2025) · Intelligent Systems with Applications · doi:10.1016/j.iswa.2025.200557 · Cited by 1
Sivapriya, C., Subbaiyan, G., & Tadepalli, S. (2025) · Intelligent Decision Technologies · doi:10.1177/18724981251322925
Carbon at the Memory Wall: Lifecycle and Aging-Aware Analysis of Processing-in-DRAM LLM Servers
Magar, B. T., Patel, S. P., & Thakkar, I. G. (2026) · International Green and Sustainable Computing Conference · doi:10.1145/3797248.3816062
Saxena, D., Chouhan, C. S., Goyal, N., Choudhary, A., & S, A. K. T. (2026) · International Conference on Innovations in Computational Intelligence (ICICI) · doi:10.1109/icici68867.2026.11564960
Metadata
- Authors
- Huang, Y., Guo, B., & Shen, Y.
- Affiliations
- Sichuan University (CN)
- Venue
- IEEE Access
- Published
- 2019
- Volume
- 7
- Pages
- 64303-64314
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W2943912161
- Cited by
- 22
- References
- 15
- Citations by year
- 2026: 3 · 2025: 4 · 2024: 3 · 2023: 2 · 2022: 3 · 2021: 3 · 2020: 4
- FWCI
- 2.55
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- IEEE Access (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- State Key Program of National Natural Science Foundation of China (61332001)
- National Natural Science Foundation of China (61772352, 61472050)
- Science and Technology Planning Project of Sichuan Province (2018ZDZX0010, 2017GZDZX0003)
- Topics
- Physical Sciences › Computer Science › Hardware and Architecture › Parallel Computing and Optimization Techniques
- Physical Sciences › Computer Science › Hardware and Architecture › Energy Efficiency in Computing
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Low-power high-performance VLSI design
- Keywords
- GPU power consumption, dynamic voltage and frequency scaling, energy management, master-slave control, energy-performance trade-off, power consumption
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