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.

Jul 13Week of Sep 28

Latest papers 153

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Measuring Accuracy and Energy-to-Solution of Quantum Fine-Tuning of Foundational AI Models
May 4, 2026Oliver Knitter, Sang Hyub Kim, Maximilian Wurzer +7Fine-Tuning Energy-Efficient ML
IonQ Inc., 4505 Campus Dr, College Park, MD 20740, USA · QuantumBasel, Schorenweg 44b, 4144 Arlesheim, Switzerland · Center for Quantum Computing and Quantum Coherence (QC2), University of Basel, Klingelbergstrasse 82, 4056 Basel, Switzerland
Silicon Showdown: Performance, Efficiency, and Ecosystem Barriers in Consumer-Grade LLM Inference
May 1, 2026Abdurrahman Javat, Allan KazakovLLM Inference Efficiency LLM Quantization
Bahcesehir University, Istanbul, Türkiye
Token Arena: A Continuous Benchmark Unifying Energy and Cognition in AI Inference
May 1, 2026Yuxuan Gao, Megan Wang, Yi Ling YuLLM Evaluation Energy-Efficient ML
University of Pennsylvania · OpenMesh AI · Columbia University
Physical Foundation Models: Fixed hardware implementations of large-scale neural networks
Apr 30, 2026Logan G Wright, Tianyu Wang, Tatsuhiro Onodera +1Efficient Neural Network Inference AI Accelerator Inference
Department of Applied Physics, Yale University, New Haven, CT 06520, USA · Department of Electrical and Computer Engineering, Boston University, Boston, MA 02215, USA · School of Applied and Engineering Physics, Cornell University, Ithaca, NY 14853, USA +2
AI Inference as Relocatable Electricity Demand: A Latency-Constrained Energy-Geography Framework
Apr 30, 2026Xubin Luo, Cheng YangEnergy-Efficient ML Cost-Aware Inference
School of Computer Science and Artificial Intelligence, Southwestern University of Finance and Economics, Chengdu, China
Green Physics-Informed Machine Learning Models For Structural Health Monitoring
Apr 30, 2026Daisy R Bradley, Elizabeth J CrossEnergy-Efficient ML Physics-Informed ML
University of Sheffield Sheffield, UK
Energy-Efficient Plant Monitoring via Knowledge Distillation
Apr 29, 2026Ilyass Moummad, Reda Bensaid, Kawtar Zaher +5Plant Disease Classification Energy-Efficient ML
LIRMM, Univ Montpellier, CNRS, Inria, Montpellier, France · IMT Atlantique, Brest, France. Polytechnique Montréal, Canada · Institut National de l’Audiovisuel, Paris, France +2
LLM-Guided Runtime Parameter Optimization for Energy-Efficient Model Inference
Apr 29, 2026Katelyn Crumpacker, Dimitrios NikolopoulosLLM Inference Efficiency Energy-Efficient ML
Virginia Polytechnic and State University
Hierarchical adaptive control for real-time dynamic inference at the edge
Apr 29, 2026Francesco Daghero, Mahyar Tourchi Moghaddam, Mikkel Baun KjærgaardAdaptive Inference Distribution Shift
MMMI Software Engineering University of Southern Denmark Odense, Denmark
EdgeSpike: Spiking Neural Networks for Low-Power Autonomous Sensing in Edge IoT Architectures
Apr 29, 2026Gustav Olaf Yunus Laitinen-Fredriksson Lundstrom-Imanov, Taner YilmazEnergy-Efficient ML Edge Inference
Department of Economics, Stockholm University, Universitetsvägen 10 A, SE-106 91 Stockholm, Sweden · Department of Computer Engineering, Afyon Kocatepe University, 03200 Afyonkarahisar, Türkiye
Performance and Energy Trade-Off Analysis of Hierarchical Federated Learning for Plant Disease Classification
Apr 28, 2026Athanasios Papanikolaou, Athanasios Tziouvaras, Pavlos Stoikos +6Hierarchical Federated Learning Plant Disease Classification
University of Zagreb, Croatia · University of Thessaly, Greece · University of Glasgow, UK +2
A Comparative Analysis on the Performance of Upper Confidence Bound Algorithms in Adaptive Deep Neural Networks
Apr 27, 2026Grigorios Papanikolaou, Ioannis Kontopoulos, Konstantinos TserpesMulti-Armed Bandits Dynamic Neural Networks
Department of Electrical and Computer Engineering National Technical University of Athens, Greece
minAction.net: Energy-First Neural Architecture Design -- From Biological Principles to Systematic Validation
Apr 27, 2026Martin G. FraschEnergy-Efficient ML Neural Network Optimization
Institute on Human Development and Disability, University of Washington, Seattle, WA 98195, USA · Health Stream Analytics, LLC, Seattle, WA, USA
ROSA: Robust and Energy-Efficient Microring-Based Optical Neural Networks via Optical Shift-and-Add and Layer-Wise Hybrid Mapping
Apr 24, 2026Huifan Zhang, Yun Hu, Caizhi Sheng +2Energy-Efficient ML Neural Network Robustness
ShanghaiTech University, Shanghai, China
Dilated CNNs for Periodic Signal Processing: A Low-Complexity Approach
Apr 23, 2026Eli Gildish, Michael Grebshtein, Igor MakienkoEnergy-Efficient ML Convolutional Neural Networks
RSL Electronics LTD, Migdal Ha'Emek, 23100, Israel
ImageHD: Energy-Efficient On-Device Continual Learning of Visual Representations via Hyperdimensional Computing
Apr 23, 2026Jebacyril Arockiaraj, Dhruv Parikh, Viktor PrasannaVisual Representation Learning Energy-Efficient ML
University of Southern California, Los Angeles, CA, USA
Watts-per-Intelligence Part II: Algorithmic Catalysis
Apr 21, 2026Elija PerrierEnergy-Efficient ML Information-Theoretic Lower Bounds
Centre for Quantum Software and Information, University of Technology Sydney, Australia.
Towards Energy Impact on AI-Powered 6G IoT Networks: Centralized vs. Decentralized
Apr 21, 2026Anjie Qiu, Donglin Wang, Sanket Partani +2Energy-Efficient ML Internet of Things
*Institute for Wireless Communication and Navigation (WiCoN), University of Kaiserslautern-Landau (RPTU) · +Intelligent Networks Research Group (IN), German Research Center for Artificial Intelligence (DFKI)
Scalable Memristive-Friendly Reservoir Computing for Time Series Classification
Apr 21, 2026Coşku Can Horuz, Andrea Ceni, Claudio Gallicchio +1Dynamic Neural Networks Energy-Efficient ML
Adaptive AI Research Group University of Lübeck, Germany · Department of Computer Science University of Pisa, Italy
Silicon Aware Neural Networks
Apr 21, 2026Sebastian Fieldhouse, Kea-Tiong TangEfficient Neural Network Inference Energy-Efficient ML
College of Semiconductor Research National Tsing Hua University · Department of Electrical Engineering National Tsing Hua University
Efficient Mixture-of-Experts LLM Inference with Apple Silicon NPUs
Apr 20, 2026Afsara Benazir, Felix Xiaozhu LinAI Accelerator Inference Energy-Efficient ML
University of Virginia Charlottesville, Virginia, USA
Mistake gating leads to energy and memory efficient continual learning
Apr 15, 2026Aaron Pache, Mark CW van RossumEnergy-Efficient ML Continual Learning
School of Mathematical Sciences University of Nottingham, Nottingham NG7 2RD, United Kingdom · School of Psychology University of Nottingham, Nottingham NG7 2RD, United Kingdom
EcoFair: Energy-Efficient Inference Routing for Edge AI under Data Degradation
Mar 27, 2026Mostafa Anoosha, Dhavalkumar Thakker, Kuniko Paxton +4Adaptive Inference Energy-Efficient ML
School of Digital and Physical Science, University of Hull Kingston upon Hull, HU6 7RX, U.K.
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
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.
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 LLM Training
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.

May 4, 2026, quant-phJ/K move · Enter open · S save

Measuring Accuracy and Energy-to-Solution of Quantum Fine-Tuning of Foundational AI Models

Oliver Knitter, Sang Hyub Kim, Maximilian Wurzer, Jonathan Mei+6

IonQ Inc., 4505 Campus Dr, College Park, MD 20740, USA · QuantumBasel, Schorenweg 44b, 4144 Arlesheim, Switzerland · Center for Quantum Computing and Quantum Coherence (QC2), University of Basel, Klingelbergstrasse 82, 4056 Basel, Switzerland

Fine-Tuning Energy-Efficient ML Hybrid Quantum-Classical ML Variational Quantum Circuits

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We present an experimental study of energy-to-solution (ETS) of hybrid quantum-classical applications, enabled by direct instrumentation of power consumption of a Forte Enterprise trapped-ion quantum processor. We apply this methodology to a hybrid quantum-classical pipeline for quantum fine-tuning of foundational AI models, and validate the approach end-to-end on quantum hardware. Despite noise and limited qubit counts, the resulting models achieve accuracy competitive with and exceeding classical baselines such as logistic regression and support vector classifiers. Our results show that QPU energy consumption scales approximately linearly with qubit number for shallow circuits, while classical simulation exhibits exponential scaling, indicating a break-even for ETS around 34 qubits. The classification error improvement of the best quantum fine-tuned model over the best classical fine-tuned model considered in this study is around 24%. We further contextualize these findings with comparisons to tensor network methods. This work establishes energy-to-solution as a measurable and scalable metric for evaluating quantum applications and provides experimental evidence of favorable energy-accuracy trade-offs.

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