Energy-Efficient ML

ML: Machine Learning

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12 papers in the last four weeks, up 9% on the four weeks before. 0.1% of all new papers.

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Fault-tolerant foundation models
Oct 7, 2026Trevor McCourt, Ila R. Fiete, Isaac L. ChuangLLM Training Language Model Scaling Laws
Department of Electrical Engineering and Computer Science, MIT · Department of Brain and Cognitive Sciences, McGovern Institute, MIT · Department of Physics, MIT
ActTune: Action-Aware Precision and GPU Operating-Point Adaptation for Energy-Efficient Vision-Language-Action Inference
Oct 6, 2026Zou Qingyun, Bin Gao, Wenju Zhao +3Energy-Efficient ML VLM Quantization
National University of Singapore · Huazhong University of Science and Technology
Choosing an energy-efficient software architecture for building system diagnostic support
Oct 5, 2026Roxane Koitz-Hristov, Franz WotawaEnergy-Efficient ML Building Energy Management
Graz University of Technology Institute of Software Engineering and Artificial Intelligence Inffeldgasse 16b/2, A-8010 Graz, Austria
Exposing the Cost of Deep Learning Audio Development
Oct 1, 2026Constance Douwes, Paul Magron, Romain SerizelEnergy-Efficient ML
Universit´e de Lorraine, CNRS, Inria, LORIA, F-54000 Nancy, France
Beyond State-of-the-Art: Standardising Environmental Impact Metrics for AI Research
Oct 1, 2026Lachlan McGinness, Dan Pagendam, Robert OffnerLLM Inference Efficiency Energy-Efficient ML
School of Computing, Australian National University · Commonwealth Scientific and Industrial Research Organisation
Beyond Energy: When Sustainability Dimensions Reshape LLM Serving Decisions
Sep 28, 2026Tianyao Shi, Xipeng Shen, Yi DingLLM Inference Efficiency LLM Serving
Elmore Family School of Electrical and Computer Engineering, Purdue University, USA
From Grey-Box to Green-Box: When can Physics-Informed Machine Learning Reduce Carbon Footprints in Structural Health Monitoring?
Sep 27, 2026Daisy R. Bradley, Nathan A. Hinchliffe, Daniel J. Pitchforth +2Energy-Efficient ML Physics-Informed ML
Dynamics Research Group, University of Sheffield, Sheffield, UK
A principled approach for energy-efficient training via phase-aware GPU frequency tuning
Sep 21, 2026Miguel Braga, Júlio Pinto, Rahma Nouaji +5Energy-Efficient ML
INESC TEC & U. Minho · McGill University
RISC-V and machine learning: a survey
Sep 17, 2026Shriman Keshri, Apparna Singh, Chinmaya Kumar Palo +2Energy-Efficient ML
National Institute of Science Education and Research, Bhubaneswar, Odisha, India · Work conducted at NISER · Sri Sri University, Cuttack, Odisha, India +2
Where Should Agents Live? Energy-Memory Characterization of Agentic AI for the Edge-Cloud Continuum
Sep 16, 2026Carolina Fortuna, Vid Hanžel, Tim Strnad +1LLM Inference Efficiency Energy-Efficient ML
Department of Communication Systems, Jožef Stefan Institute, Jamova cesta 39, 1000 Ljubljana, Slovenia
A Game-Theoretic Framework for Incentive-Compatible AI training Under Renewable-Energy Constraints
Sep 14, 2026Konstantinos Varsos, Ramin Khalili, Adamantia Stamou +2Mechanism Design Energy-Efficient ML
Athens University of Economics and Business, Greece · Independent researcher, Munich Germany
One Simple Trick for Improving the Performance of Energy-Limited Local Inference and Training
Sep 14, 2026Erik Schultheis, Maximilian Kleinegger, Dan AlistarhEnergy-Efficient ML GPU Acceleration
ISTA · TU Wien · RedHat AI
Adaptive AI: Energy Efficient Multi-exit TinyML on Intelligent Vision Systems at the Edge
Sep 14, 2026Luca Crupi, Lorenzo Lamberti, Alessandro Giusti +1Efficient Neural Network Inference Energy-Efficient ML
IDSIA, SUPSI, Lugano, Switzerland · IIS, ETH Zürich, Switzerland
The Battery Price of edge AI: A study of the Environmental Impact of LLM Inference on Mobile Devices
Sep 14, 2026Édouard Guégain, Tristan CoignionOn-Device Language Model Inference Energy-Efficient ML
DRI, GREENSPECTOR · Univ. Bordeaux, CNRS, Bordeaux INP, LaBRI, UMR 5800, Talence, France
Characterizing Job Power Elasticity for Power-Flexible AI Training
Sep 12, 2026Philip Colangelo, Charles Dawson, Shayan Sengupta +2Energy-Efficient ML Efficient Language Model Training
Emerald AI
Para-Pipe: Exploiting Hierarchical Operator Parallelism of ML Computational Graphs on SoCs
Sep 3, 2026Yujie Zhang, Huiying Lan, Ehsan Aghapour +5Efficient Neural Network Inference Energy-Efficient ML
School of Computing, National University of Singapore, Singapore · Parallel Computer Systems, the University of Amsterdam, 1098 XH Amsterdam, The Netherlands · Information Technology Department, Black Sesame Technologies, San Jose, CA 95131, USA
Artificial Intelligence for Energy Optimization in Data Centers
Sep 3, 2026Mohammed Basharath Ullah, Summaiya Unnisa Begum, Mohammed Nadeem UllahEnergy-Efficient ML
Hyderabad, India
Grounded, Compute-Efficient LLM Policy Agents for Energy-Poverty Equity in Physically-Constrained Peer-to-Peer Energy Markets
Sep 1, 2026Kunal Jadhav, Siddhesh MoreEnergy-Efficient ML LLM Agents
Arizona State University
A Checklist to assess the energy and carbon impacts of ML/AI applications in Earth System Modeling
Sep 1, 2026Filippo Dainelli, Amirpasha Mozaffari, Marina Castaño +5Energy-Efficient ML Responsible AI
Earth Department, Barcelona Supercomputing Center (BSC), Barcelona, Spain
Stress-Testing Efficient Responsible-AI Evaluation: When Compute Savings Change Benchmark Conclusions
Aug 31, 2026Ahmed El Kady, Aravind Narayanan, Rehana Noorani +2LLM Evaluation Energy-Efficient ML
Vector Institute · Independent researcher · University of Calgary
On the Instance Hardness as a Decision Criterion in TinyML Systems
Aug 30, 2026Tobiasz Puslecki, Krzysztof WalkowiakEnergy-Efficient ML Inference-Time Optimization
Department of Systems and Computer Networks, Wroclaw University of Science and Technology
Towards an Expressivity-Normalized Energy-Demand Comparison of ANNs and SNNs
Aug 30, 2026Miriam Kranzlmüller, Pascal Esser, Gitta KutyniokEfficient Neural Network Inference ReLU Neural Networks
Department of Mathematics LMU Munich, Germany · *Munich Center for Machine Learning (MCML) · †Department of Physics and Technology, University of Tromsø, Norway, DLR-German Aerospace Center, Germany
The thermodynamic freedom of a thermodynamic computer
Aug 28, 2026Stephen WhitelamEfficient Inference Thermodynamic Computing
Molecular Foundry, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA 94720, USA
InFactPlanner: Planning Sustainable Geo-Distributed LLM Data Centers
Aug 13, 2026Nicoletta Tsiopani, Moysis Symeonides, George Pallis +1Energy-Efficient ML LLM Inference Serving
Department of Computer Science University of Cyprus
Quantifying the Relationship Between Clinical Safety and Environmental Impact in Therapeutic LLMs
Aug 12, 2026Alireza A. Safaei, Laura M. Vowels, Matthew J. Vowels +2Energy-Efficient ML LLM Safety Evaluation
University of Isfahan Isfahan, Iran · University of Roehampton London, UK · Kivira Health New York City, New York, U.S.A.
XGBoost "is all you need": the case of forecasting transmitted heat energy in District Heating Systems
Aug 11, 2026Milan ZdravkovićGradient-Boosted Decision Trees Energy-Efficient ML
Faculty of Mechanical Engineering University of Niš ul. Aleksandra Medvedeva 14, 18000 Niš, Serbia
Eco-SoC: A Sustainable VLSI Architecture for Energy-Proportional Artificial Intelligence
Aug 9, 2026Jatin ChopraEnergy-Efficient ML
Microsoft Corporation, USA · Indian Institute of Technology Delhi, India
Towards Sustainable Artificial Intelligence: A Comprehensive Review and Comparative Analysis of Deep Learning Models' Carbon Footprint
Aug 7, 2026Samar Garrab, Sarra Boughriou, Manel BenSassiEnergy-Efficient ML
Royal Military College of Canada, Ontario, Canada. · Concordia University, Quebec, Canada. · ´Ecole Nationale des Sciences de l’Informatique, Tunisia.
Energy and Performance Benchmarking of Deep Learning Models for Breast Cancer Detection
Aug 7, 2026Samar Garrab, Ghada AchourBreast Cancer Energy-Efficient ML
Royal Military College of Canada Kingston, ON, Canada · National Engineering School of Sousse Sousse, Tunisia
Energy- and Memory-Efficient PEFT Methods for Personalized On-Device SLMs on Consumer GPUs
Aug 5, 2026Kuanysh Akhmetzhanov, Jurn-Gyu ParkFine-Tuning On-Device Language Model Inference
Department of Computer Science, Nazarbayev University, Astana 010000, Kazakhstan

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Fault-tolerant foundation models

Trevor McCourt, Ila R. Fiete, Isaac L. Chuang

Department of Electrical Engineering and Computer Science, MIT · Department of Brain and Cognitive Sciences, McGovern Institute, MIT · Department of Physics, MIT

LLM Training Language Model Scaling Laws Energy-Efficient ML

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Emerging computer hardware often trades reliability for energy efficiency; here we show that large-language models (LLMs) can be trained to tolerate this unreliability, and that rather than degrading, their error resilience actually increases as they grow. Modified neural scaling laws inferred from 40,000 GPU-hours of training runs on simulated faulty digital hardware quantify this trend and suggest that models learn to compute within "good" error-correcting codes, whose relative overhead remains finite no matter how large the model gets. This finding leads us to conjecture that appropriately trained LLMs may be formally fault-tolerant; if true, running AI inference on low energy, faulty hardware may be a path to substantial energy savings over the status quo.

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