Energy-Efficient ML

ML: Machine Learning

Energy-efficient machine learning studies how to reduce the resources required to train and run models, especially on edge devices and at large serving scale. Common approaches include pruning, quantization, distillation, sparse and early-exit models, efficient scheduling and routing, and hardware-software co-design for GPUs, accelerators, and neuromorphic chips. Researchers increasingly measure energy on real hardware and assess carbon and other environmental impacts across the model life cycle, since proxies such as FLOPs do not reliably predict deployment costs. The central challenge is balancing energy, accuracy, latency, and memory while determining whether efficiency gains translate into lower total impact as usage grows.

Latest papers

257 papers17 papers in the last four weeks, down 23% on the four weeks before. 0.1% of all new papers.

AI-CARE: Carbon-Aware Reporting Evaluation Metric for AI Models
Feb 17, 2026KC Santosh, Srikanth Baride, Rodrigue RizkCarbon-Aware Computing Energy-Efficient ML
USD Artificial Intelligence Research, Department of Computer Science University of South Dakota Vermillion, SD 57069, USA
DART: Input-Difficulty-AwaRe Adaptive Threshold for Early-Exit DNNs
Feb 17, 2026Parth Patne, Mahdi Taheri, Christian Herglotz +3Efficient Neural Network Inference Early-Exit Neural Networks
Brandenburg Technical University, Cottbus, Germany · Tallinn University of Technology, Tallinn, Estonia · Leibniz Institute for High Performance Microelectronics, Frankfurt Oder, Germany
GaiaFlow: Semantic-Guided Diffusion Tuning for Carbon-Frugal Search
Feb 17, 2026Rong Fu, Jia Yee Tan, Chunlei Meng +4Information Retrieval Energy-Efficient ML
Independent Researcher
Is Knowledge Distillation Actually Greener? A Case Study in Machine Translation
Feb 10, 2026Joseph Attieh, Timothee Mickus, Anne-Laure Ligozat +2Energy-Efficient ML Knowledge Distillation
University of Helsinki · Université Paris-Saclay, CNRS, LISN
Magneton: Optimizing Energy Efficiency of ML Systems via Differential Energy Debugging
Dec 9, 2025Yi Pan, Wenbo Qian, Dedong Xie +3Energy-Efficient ML
University of Washington · Shanghai Jiao Tong University · Boston University
Energy-Efficient Federated Learning via Adaptive Encoder Freezing for MRI-to-CT Conversion: A Green AI-Guided Research
Nov 25, 2025Ciro Benito Raggio, Lucia Migliorelli, Nils Skupien +6Energy-Efficient ML Federated Learning
Institute of Biomedical Engineering Karlsruhe Institute of Technology Fritz-Haber-Weg 1, Karlsruhe 76131 Baden-Württemberg, Germany · Department of Political Science Università Degli Studi Di Teramo Via Renato Balizarini 1, Teramo 64100 Italy · Department of Radiation Oncology University Medical Center Schleswig-Holstein Feldstrasse 21, Kiel 24105 Schleswig-Holstein, Germany +1
Computation-aware Energy-harvesting Federated Learning with Pipelined Cyclic Scheduling
Nov 14, 2025Eunjeong Jeong, Nikolaos PappasEnergy-Efficient ML Federated Learning
E. Jeong and N. Pappas are with the Department of Computer and Information Science, Linköping University, Sweden.
Energy-Efficient Deep Learning Without Backpropagation: A Rigorous Hardware-Validated Benchmarking Study of Forward-Only Algorithms
Nov 2, 2025Przemysław Spyra, Witold DzwinelEnergy-Efficient ML Forward-Forward Learning
AGH University of Krakow, Faculty of Computer Science
Energy Overhead in Sensor Nodes: Embedded Machine Learning and Data Offloading via Low Power Wide Area Networks
Oct 28, 2025Benjamin Karic, Nina Herrmann, Jan Stenkamp +3Internet of Things Energy-Efficient ML
University of Münster · re:edu GmbH & Co. KG
A Vision Toward Energy-Efficient Domain-Specific Artificial Intelligence Models and Agents
Oct 24, 2025Abhijit Chatterjee, Niraj K. Jha, Jonathan D. Cohen +6Energy-Efficient ML Lifelong Learning Agents
School of Electrical and Computer Engineering, Georgia Institute of Technology · Department of Electrical and Computer Engineering, Princeton University · Department of Psychology & Neuroscience Institute, Princeton University +4
The Environmental Impacts of Language Model Training Keep Rising Now is the Time to Catch Impacts on the Rebound
Oct 10, 2025Clément Morand, Anne-Laure Ligozat, Aurélie NévéolEnergy-Efficient ML Life Cycle Assessment
STL · Université Paris-Saclay, CNRS, LISN · ENSIIE, LISN, STL +2
VoltanaLLM: Energy-Efficient and SLO-Aware Disaggregated LLM Serving via Adaptive Frequency Control and State-Space Routing
Sep 5, 2025Jiahuan Yu, Aryan Taneja, Junfeng Lin +1LLM Serving Energy-Efficient ML
Siebel School of Computing and Data Science, University of Illinois Urbana-Champaign, Champaign, USA · Department of Precision Instrument, Tsinghua University, Beijing, China
Energy-Efficient Real-Time 4-Stage Sleep Classification at 10-Second Resolution
Aug 6, 2025Zahra Mohammadi, Parnian Fazel, Siamak MohammadiSleep Stage Classification Energy-Efficient ML
School of Electrical and Computer Engineering, University of Tehran.
Information Thermodynamics of Agents: The Work Capacity of Channels with Memory
Apr 8, 2025Lukas J. Fiderer, Paul C. Barth, Isaac D. Smith +1Energy-Efficient ML
Universität Innsbruck, Institut für Theoretische Physik, Technikerstraße 21a, A-6020 Innsbruck, Austria
A Comprehensive Evaluation of Deep Learning Object Detection Models on Heterogeneous Edge Devices
Sep 25, 2024Daghash K. Alqahtani, Muhammad Aamir Cheema, Maria A. Rodriguez +1Energy-Efficient ML Edge Inference
DisNet Lab, School of Computing and Information Systems, The University of Melbourne, Melbourne, VIC, Australia · Monash University, Melbourne, VIC, Australia
Application based Evaluation of an Efficient Spike-Encoder, "Spiketrum"
May 24, 2024MHD Anas Alsakkal, Runze Wang, Jayawan Wijekoon +1Lossless Data Compression Spike-Based Signal Encoding
Department of Electrical and Electronic Engineering, The University of Manchester, Manchester, UK. · State Key Lab of Brain-Machine Intelligence, College of Computer Science and Technology, Zhejiang University, Hangzhou, China.
A Survey of Transformer-based Language Models with Focus on Efficiency
May 15, 2024Wazib Ansar, Saptarsi Goswami, Amlan ChakrabartiLLM Compression Energy-Efficient ML
A. K. Choudhury School of IT University of Calcutta, Kolkata, India · Department of Computer Science Bangabasi Morning College Kolkata, India

Feb 17, 2026J/K move · Enter open · S save

AI-CARE: Carbon-Aware Reporting Evaluation Metric for AI Models

KC Santosh, Srikanth Baride, Rodrigue Rizk

USD Artificial Intelligence Research, Department of Computer Science University of South Dakota Vermillion, SD 57069, USA

Carbon-Aware Computing Energy-Efficient ML Multi-Objective Optimization

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As machine learning (ML) continues its rapid expansion, the environmental cost of model training and inference has become a critical societal concern. Existing benchmarks overwhelmingly focus on standard performance metrics such as accuracy, BLEU, or mAP, while largely ignoring energy consumption and carbon emissions. This single-objective evaluation paradigm is increasingly misaligned with the practical requirements of large-scale deployment, particularly in energy-constrained environments such as mobile devices, developing regions, and climate-aware enterprises. In this paper, we propose AI-CARE, an evaluation tool for reporting energy consumption, and carbon emissions of ML models. In addition, we introduce the carbon-performance tradeoff curve, an interpretable tool that visualizes the Pareto frontier between performance and carbon cost. We demonstrate, through theoretical analysis and empirical validation on representative ML workloads, that carbon-aware benchmarking changes the relative ranking of models and encourages architectures that are simultaneously accurate and environmentally responsible. Our proposal aims to shift the research community toward transparent, multi-objective evaluation and align ML progress with global sustainability goals. The tool and documentation are available at https://github.com/USD-AI-ResearchLab/ai-care.