Enhancing Energy-Awareness in Deep Learning through Fine-Grained Energy Measurement
Fuente:
arXiv
Saved in:
| Main Authors: | Rajput, Saurabhsingh, Widmayer, Tim, Shang, Ziyuan, Kechagia, Maria, Sarro, Federica, Sharma, Tushar |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
FlipFlop: A Static Analysis-based Energy Optimization Framework for GPU Kernels
by: Rajput, Saurabhsingh, et al.
Published: (2026)
by: Rajput, Saurabhsingh, et al.
Published: (2026)
CodeGreen: Towards Improving Precision and Portability in Software Energy Measurement
by: Rajput, Saurabhsingh, et al.
Published: (2026)
by: Rajput, Saurabhsingh, et al.
Published: (2026)
Energy Flow Graph: Modeling Software Energy Consumption
by: Rajput, Saurabhsingh, et al.
Published: (2026)
by: Rajput, Saurabhsingh, et al.
Published: (2026)
Tu(r)ning AI Green: Exploring Energy Efficiency Cascading with Orthogonal Optimizations
by: Rajput, Saurabhsingh, et al.
Published: (2025)
by: Rajput, Saurabhsingh, et al.
Published: (2025)
A Validated Taxonomy on Software Energy Smells
by: Mehditabar, Mohammadjavad, et al.
Published: (2026)
by: Mehditabar, Mohammadjavad, et al.
Published: (2026)
Smart but Costly? Benchmarking LLMs on Functional Accuracy and Energy Efficiency
by: Mehditabar, Mohammadjavad, et al.
Published: (2025)
by: Mehditabar, Mohammadjavad, et al.
Published: (2025)
On the Compression of Language Models for Code: An Empirical Study on CodeBERT
by: d'Aloisio, Giordano, et al.
Published: (2024)
by: d'Aloisio, Giordano, et al.
Published: (2024)
COMET: Generating Commit Messages using Delta Graph Context Representation
by: Mandli, Abhinav Reddy, et al.
Published: (2024)
by: Mandli, Abhinav Reddy, et al.
Published: (2024)
Energy Consumption of Dataframe Libraries for End-to-End Deep Learning Pipelines:A Comparative Analysis
by: Kumar, Punit, et al.
Published: (2025)
by: Kumar, Punit, et al.
Published: (2025)
This Is Taking Too Long -- Investigating Time as a Proxy for Energy Consumption of LLMs
by: Krupp, Lars, et al.
Published: (2026)
by: Krupp, Lars, et al.
Published: (2026)
Interpreting Performance Profiles with Deep Learning
by: Liu, Zhuoran
Published: (2025)
by: Liu, Zhuoran
Published: (2025)
Energy Patterns for Web: An Exploratory Study
by: Rani, Pooja, et al.
Published: (2024)
by: Rani, Pooja, et al.
Published: (2024)
Estimating the Energy Footprint of Software Systems: a Primer
by: Castor, Fernando
Published: (2024)
by: Castor, Fernando
Published: (2024)
What Is the Cost of Energy Monitoring? An Empirical Study on the Overhead of RAPL-Based Tools
by: Diamond, Jeremy, et al.
Published: (2026)
by: Diamond, Jeremy, et al.
Published: (2026)
An Empirical Study on How Architectural Topology Affects Microservice Performance and Energy Usage
by: Ristova, Irena, et al.
Published: (2026)
by: Ristova, Irena, et al.
Published: (2026)
Energy-Efficient Software Development: A Multi-dimensional Empirical Analysis of Stack Overflow
by: Jin, Bihui, et al.
Published: (2024)
by: Jin, Bihui, et al.
Published: (2024)
Broken Windows: Exploring the Applicability of a Controversial Theory on Code Quality
by: Spinellis, Diomidis, et al.
Published: (2024)
by: Spinellis, Diomidis, et al.
Published: (2024)
MNN-AECS: Energy Optimization for LLM Decoding on Mobile Devices via Adaptive Core Selection
by: Huang, Zhengxiang, et al.
Published: (2025)
by: Huang, Zhengxiang, et al.
Published: (2025)
Predicting Software Performance with Divide-and-Learn
by: Gong, Jingzhi, et al.
Published: (2023)
by: Gong, Jingzhi, et al.
Published: (2023)
Test-based Patch Clustering for Automatically-Generated Patches Assessment
by: Martinez, Matias, et al.
Published: (2022)
by: Martinez, Matias, et al.
Published: (2022)
Empirical and Sustainability Aspects of Software Engineering Research in the Era of Large Language Models: A Reflection
by: Williams, David, et al.
Published: (2025)
by: Williams, David, et al.
Published: (2025)
Charting The Evolution of Solidity Error Handling
by: Mitropoulos, Charalambos, et al.
Published: (2024)
by: Mitropoulos, Charalambos, et al.
Published: (2024)
VecTrans: Enhancing Compiler Auto-Vectorization through LLM-Assisted Code Transformations
by: Zheng, Zhongchun, et al.
Published: (2025)
by: Zheng, Zhongchun, et al.
Published: (2025)
Who Wins the Race? (R Vs Python) - An Exploratory Study on Energy Consumption of Machine Learning Algorithms
by: Chattaraj, Rajrupa, et al.
Published: (2025)
by: Chattaraj, Rajrupa, et al.
Published: (2025)
Investigating Execution-Aware Language Models for Code Optimization
by: Di Menna, Federico, et al.
Published: (2025)
by: Di Menna, Federico, et al.
Published: (2025)
SWE-fficiency: Can Language Models Optimize Real-World Repositories on Real Workloads?
by: Ma, Jeffrey Jian, et al.
Published: (2025)
by: Ma, Jeffrey Jian, et al.
Published: (2025)
PerfBench: Can Agents Resolve Real-World Performance Bugs?
by: Garg, Spandan, et al.
Published: (2025)
by: Garg, Spandan, et al.
Published: (2025)
Root Cause Localization for Microservice Systems in Cloud-edge Collaborative Environments
by: Zhu, Yuhan, et al.
Published: (2024)
by: Zhu, Yuhan, et al.
Published: (2024)
Prompting for Performance: Exploring LLMs for Configuring Software
by: Spieker, Helge, et al.
Published: (2025)
by: Spieker, Helge, et al.
Published: (2025)
Do AI Models Dream of Faster Code? An Empirical Study on LLM-Proposed Performance Improvements in Real-World Software
by: Yi, Lirong, et al.
Published: (2025)
by: Yi, Lirong, et al.
Published: (2025)
Should AI Optimize Your Code? A Comparative Study of Classical Optimizing Compilers Versus Current Large Language Models
by: Rosas, Miguel Romero, et al.
Published: (2024)
by: Rosas, Miguel Romero, et al.
Published: (2024)
Can We Make Code Green? Understanding Trade-Offs in LLMs vs. Human Code Optimizations
by: Rani, Pooja, et al.
Published: (2025)
by: Rani, Pooja, et al.
Published: (2025)
An Empirical Study on the Performance and Energy Usage of Compiled Python Code
by: Stoico, Vincenzo, et al.
Published: (2025)
by: Stoico, Vincenzo, et al.
Published: (2025)
Learning Performance-Improving Code Edits
by: Shypula, Alexander, et al.
Published: (2023)
by: Shypula, Alexander, et al.
Published: (2023)
MLKAPS: Machine Learning and Adaptive Sampling for HPC Kernel Auto-tuning
by: Jam, Mathys, et al.
Published: (2025)
by: Jam, Mathys, et al.
Published: (2025)
FaaSter Troubleshooting -- Evaluating Distributed Tracing Approaches for Serverless Applications
by: Borges, Maria C., et al.
Published: (2021)
by: Borges, Maria C., et al.
Published: (2021)
Why Attention Fails: A Taxonomy of Faults in Attention-Based Neural Networks
by: Jahan, Sigma, et al.
Published: (2025)
by: Jahan, Sigma, et al.
Published: (2025)
DeepGD: A Multi-Objective Black-Box Test Selection Approach for Deep Neural Networks
by: Aghababaeyan, Zohreh, et al.
Published: (2023)
by: Aghababaeyan, Zohreh, et al.
Published: (2023)
Risk-Aware Batch Testing for Performance Regression Detection
by: Sayedsalehi, Ali, et al.
Published: (2026)
by: Sayedsalehi, Ali, et al.
Published: (2026)
gigiProfiler: Diagnosing Performance Issues by Uncovering Application Resource Bottlenecks
by: Hu, Yigong, et al.
Published: (2025)
by: Hu, Yigong, et al.
Published: (2025)
Similar Items
-
FlipFlop: A Static Analysis-based Energy Optimization Framework for GPU Kernels
by: Rajput, Saurabhsingh, et al.
Published: (2026) -
CodeGreen: Towards Improving Precision and Portability in Software Energy Measurement
by: Rajput, Saurabhsingh, et al.
Published: (2026) -
Energy Flow Graph: Modeling Software Energy Consumption
by: Rajput, Saurabhsingh, et al.
Published: (2026) -
Tu(r)ning AI Green: Exploring Energy Efficiency Cascading with Orthogonal Optimizations
by: Rajput, Saurabhsingh, et al.
Published: (2025) -
A Validated Taxonomy on Software Energy Smells
by: Mehditabar, Mohammadjavad, et al.
Published: (2026)