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ISSN Approved Journal No: 2320-2882 | Impact factor: 7.97 | ESTD Year: 2013
Call For Paper - Volume 14 | Issue 3 | Month- March 2026
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Paper Title
WORKLOAD PREDICTION FOR DYNAMIC VOLTAGE AND FREQUENCY SCALING FOR MULTICORE SYSTEMS USING REGRESSION-BASED PREDICTION MODELS
Authors
VINOLYA S, Dr. R JAYAGOWRI
Keywords
Dynamic Voltage and Frequency Scaling (DVFS), Workload Prediction, PARSEC Benchmark, Multicore Systems, Regression Model, Machine Learning.
Abstract
Artificial Intelligence (AI) workloads place heavy demands on computational hardware, necessitating advanced power management strategies like Dynamic Voltage and Frequency Scaling (DVFS). This paper presents a workload prediction model for multicore systems based on metrics derived from simulations using the PARSEC benchmark suite and gem5. The proposed regression-based model uses system-level metrics such as IPC, CPI, cache misses, and memory access rates to accurately predict workload performance. The results show a high degree of accuracy in workload prediction, demonstrating the efficacy of machine learning models in optimizing energy efficiency through DVFS.
IJCRT's Publication Details
Unique Identification Number - IJCRT2509585
Paper ID - 291008
Page Number(s) - f141-f151
Pubished in - Volume 13 | Issue 9 | September 2025
DOI (Digital Object Identifier) - https://doi.org/10.56975/ijcrt.v13i9.291008
Publisher Name - IJCRT | www.ijcrt.org | ISSN : 2320-2882
E-ISSN Number - 2320-2882
Cite this article
VINOLYA S, Dr. R JAYAGOWRI, "WORKLOAD PREDICTION FOR DYNAMIC VOLTAGE AND FREQUENCY SCALING FOR MULTICORE SYSTEMS USING REGRESSION-BASED PREDICTION MODELS", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.13, Issue 9, pp.f141-f151, September 2025, Available at :http://www.ijcrt.org/papers/IJCRT2509585.pdf
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Submit Manuscript Online Impact Factor: 7.97 Review Results : Within 02-03 Days Paper Publication : Within 02-03 Days
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ISSN: 2320-2882 Impact Factor: 7.97 and ISSN APPROVED Journal Starting Year (ESTD) : 2013
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ISSN: 2320-2882 Impact Factor: 7.97 and ISSN APPROVED Journal Starting Year (ESTD) : 2013
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