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Learning-Based, Fine-Grain Power Modeling of System-Level Hardware IPs
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/978-0-387-45528-0openalex-snapshot
Pattern Recognition and Machine Learning
inferredcites →https://doi.org/10.1007/978-1-4615-5685-5openalex-snapshot
Low Power Design in Deep Submicron Electronics
inferredcites →https://doi.org/10.1080/713827175openalex-snapshot
Feature selection for the naive bayesian classifier using decision trees
inferredcites →https://doi.org/10.1109/92.820758openalex-snapshot
Power modeling for high-level power estimation
inferredcites →https://doi.org/10.1109/aspdac.2007.358040openalex-snapshot
High-Level Power Estimation and Low-Power Design Space Exploration for FPGAs
inferredcites →https://doi.org/10.1109/cgo11327.2006openalex-snapshot
International Symposium on Code Generation and Optimization (CGO'06)
inferredcites →https://doi.org/10.1109/dsd.2014.89openalex-snapshot
Data-and State-Dependent Power Characterisation and Simulation of Black-Box RTL IP Components at System Level
inferredcites →https://doi.org/10.1109/fpl.2010.69openalex-snapshot
PrEsto: An FPGA-accelerated Power Estimation Methodology for Complex Systems
inferredcites →https://doi.org/10.1109/iccad.2004.1382659openalex-snapshot
Power estimation for cycle-accurate functional descriptions of hardware
inferredcites →https://doi.org/10.1109/iccd.2007.4601959openalex-snapshot
System level power estimation methodology with H.264 decoder prediction IP case study
inferredcites →https://doi.org/10.1109/icmla.2008.154openalex-snapshot
Decision Tree Ensemble: Small Heterogeneous Is Better Than Large Homogeneous
inferredcites →https://doi.org/10.1109/icvd.2001.902666openalex-snapshot
Accurate power macro-modeling techniques for complex RTL circuits
inferredcites →https://doi.org/10.1109/icvd.2003.1183173openalex-snapshot
Efficient RTL power estimation for large designs
inferredcites →https://doi.org/10.1109/mascots.2013.75openalex-snapshot
Introducing DVFS-Management in a Full-System Simulator
inferredcites →https://doi.org/10.1109/mdat.2023.3238340openalex-snapshot
The 2022 International Conference on Computer-Aided Design (ICCAD)
inferredcites →https://doi.org/10.1109/samos.2014.6893210openalex-snapshot
An ESL timing & power estimation and simulation framework for heterogeneous socs
inferredcites →https://doi.org/10.1109/samos.2015.7363698openalex-snapshot
ESL power estimation using virtual platforms with black box processor models
inferredcites →https://doi.org/10.1109/samos42491.2017openalex-snapshot
2017 International Conference on Embedded Computer Systems: Architectures, Modeling, and Simulation (SAMOS)
inferredcites →https://doi.org/10.1109/tcad.2016.2578882openalex-snapshot
Source-Level Performance, Energy, Reliability, Power and Thermal (PERPT) Simulation
inferredcites →https://doi.org/10.1109/tvlsi.2009.2039153openalex-snapshot
A Multi-Granularity Power Modeling Methodology for Embedded Processors
inferredcites →https://doi.org/10.1145/2024724.2024809openalex-snapshot
Transaction level statistical analysis for efficient micro-architectural power and performance studies
inferredcites →https://doi.org/10.1145/2445572.2445577openalex-snapshot
The McPAT Framework for Multicore and Manycore Architectures
inferredcites →https://doi.org/10.1145/2463209.2488804openalex-snapshot
Creation of ESL power models for communication architectures using automatic calibration
inferredcites →https://doi.org/10.1145/348019.348081openalex-snapshot
Regression-based RTL power modeling
inferredcites →https://doi.org/10.1155/2011/569031openalex-snapshot
A Model-Driven Approach for Hybrid Power Estimation in Embedded Systems Design
inferredcites →https://doi.org/10.1561/0600000020openalex-snapshot
Bilateral Filtering: Theory and Applications
inferredcites →https://doi.org/10.15781/t2rc7bopenalex-snapshot
Performance, power, and confidence modeling of digital designs
inferredcites →https://doi.org/10.5555/1953048.2078195openalex-snapshot
Scikit-learn: Machine Learning in Python
inferredcites →https://doi.org/10.5555/244522.244919openalex-snapshot
Statistical sampling and regression analysis for RT-level power evaluation
inferredcites →https://doi.org/10.5555/2755753.2757074openalex-snapshot
Dynamic power and performance back-annotation for fast and accurate functional hardware simulation
inferredcites →https://doi.org/10.5555/2840819.2840937openalex-snapshot
Learning-Based Power Modeling of System-Level Black-Box IPs
inferredcites →https://doi.org/10.7873/date.2015.0924openalex-snapshot
Dynamic Power and Performance Back-Annotation for Fast and Accurate Functional Hardware Simulation
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1007/978-3-031-13074-8_18openalex-snapshot
ML for System-Level Modeling
inferred← citeshttps://doi.org/10.1007/s11227-022-04798-5openalex-snapshot
High-level power estimation techniques in embedded systems hardware: an overview
inferred← citeshttps://doi.org/10.1109/ecmi68341.2026.11603132openalex-snapshot
Machine Learning-Based Early-Stage Power Estimation for VLSI Design Using Netlist Analysis
inferred← citeshttps://doi.org/10.1109/edpee65754.2025.00039openalex-snapshot
Research on Fusion Algorithm of Machine Learning and Oilfield Fluid Property Estimation
inferred← citeshttps://doi.org/10.1109/inspire67328.2025.11300551openalex-snapshot
Machine Learning Based Power Prediction for RTL-Level MIPS Processor Design
inferred← citeshttps://doi.org/10.1109/iseda59274.2023.10218595openalex-snapshot
Power Prediction of RTL-Level Circuits by Using Machine Learning
inferred← citeshttps://doi.org/10.1109/mlcad48534.2019.9142100openalex-snapshot
Learning-Based CPU Power Modeling
inferred← citeshttps://doi.org/10.1109/mlcad52597.2021.9531109openalex-snapshot
Fast and Accurate PPA Modeling with Transfer Learning
inferred← citeshttps://doi.org/10.1109/tc.2022.3185572openalex-snapshot
Machine Learning-Based Microarchitecture- Level Power Modeling of CPUs
inferred← citeshttps://doi.org/10.1109/tcad.2021.3124762openalex-snapshot
MLCAD: A Survey of Research in Machine Learning for CAD Keynote Paper
inferred← citeshttps://doi.org/10.1145/3451179openalex-snapshot
Machine Learning for Electronic Design Automation: A Survey
inferred← citeshttps://doi.org/10.1145/3607888openalex-snapshot
Proceedings of the 2023 International Conference on Hardware/Software Codesign and System Synthesis
inferred← citeshttps://doi.org/10.1145/3607888.3608962openalex-snapshot
Special Session: Machine Learning for Embedded System Design
inferred← citeshttps://doi.org/10.1145/3650400.3650647openalex-snapshot
A Software and Hardware Classification Algorithm for Computer Systems Based on Improved PSO model
inferred← citeshttps://doi.org/10.23919/date51398.2021.9474028openalex-snapshot
Long Short-Term Memory Neural Network-based Power Forecasting of Multi-Core Processors
inferred← citeshttps://doi.org/10.70675/ac6962caz18dez4953za3bczcf420b7284beopenalex-snapshot
Power Consumption Modeling in Embedded Systems Hardware
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