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.

Energy-Efficient Federated Edge Learning For Small-Scale Datasets in Large IoT Networks
Apr 12, 2026Haihui Xie, Wenkun Wen, Shuwu Chen +2Federated Learning Energy-Efficient ML
College of Computer and Information Sciences, Fujian Agriculture and Forestry University, and also with the Engineering Research Center of Smart Sensing and Agricultural Chip Technology, Fujian Province University, Fuzhou 350002, China · R&D Department, Techphant Technologies Company Ltd., Guangzhou 510310, China · School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou 510006, China
MoEITS: A Green AI approach for simplifying MoE-LLMs
Apr 12, 2026Luis Balderas, Miguel Lastra, José M. BenítezLLM Compression Mixture-of-Experts Pruning
Department of Computer Science and Artificial Intelligence, DiCITS, iMUDS, DaSCI, University of Granada, Granada, Spain 18071 · Department of Software Engineering, DiCITS, iMUDS, DaSCI, University of Granada, Granada, Spain 18071
End-to-end Automated Deep Neural Network Optimization for PPG-based Blood Pressure Estimation on Wearables
Apr 11, 2026Francesco Carlucci, Giovanni Pollo, Xiaying Wang +6Cuffless Blood Pressure Estimation Wearable Health Monitoring
Interuniversity Department of Regional and Urban Studies and Planning at Politecnico di Torino, Torino, Italy · Department of Control and Computer Engineering at Politecnico di Torino, Torino, Italy · Department of Information Technology and Electrical Engineering at ETH Zurich, Zurich, Switzerland +1
Watt Counts: Energy-Aware Benchmark for Sustainable LLM Inference on Heterogeneous GPU Architectures
Apr 10, 2026Mauricio Fadel Argerich, Jonathan Fürst, Marta Patiño-MartínezEnergy-Aware LLM Inference LLM Inference Efficiency
Universidad Politécnica de Madrid, C. de los Ciruelos, Boadilla del Monte, 28660, Madrid, Spain · Zurich University of Applied Sciences, Switzerland, Gertrudstrasse 15, Winterthur, 8400, Zurich, Switzerland
Ge $^\text{2}$ mS-T: Multi-Dimensional Grouping for Ultra-High Energy Efficiency in Spiking Transformer
Apr 10, 2026Zecheng Hao, Shenghao Xie, Kang Chen +3Spiking Transformers Spiking Neural Networks
Peking University, School of Computer Science, State Key Laboratory for Multimedia Information Processing, Beijing, China · Peking University, School of Computer Science, Beijing Key Laboratory of Brain-inspired Spiking Large Models, Beijing, China
Pruning Extensions and Efficiency Trade-Offs for Sustainable Time Series Classification
Apr 9, 2026Raphael Fischer, Angus Dempster, Sebastian Buschjäger +3Time Series Classification Energy-Efficient ML
Lamarr Institute for ML and AI, TU Dortmund University, Dortmund, Germany · Department of Data Science & AI, Monash University, Melbourne, Australia · Department of Pulmonary Medicine, University Medicine Essen, Essen, Germany +1
Ensembles at Any Cost? Accuracy-Energy Trade-offs in Recommender Systems
Apr 9, 2026Jannik Nitschke, Lukas Wegmeth, Joeran BeelRecommender Systems Ensemble Learning
University of Siegen, Siegen, Germany
Networking-Aware Energy Efficiency in Agentic AI Inference: A Survey
Apr 9, 2026Xiaojing Chen, Haiqi Yu, Wei Ni +5Energy-Efficient ML LLM Inference Efficiency
Shanghai University, Shanghai, China · Nanyang Technological University, Singapore · Edith Cowan University, Perth, Australia +2
Trilinear Compute-in-Memory Architecture for Energy-Efficient Transformer Acceleration
Apr 8, 2026Md Zesun Ahmed Mia, Jiahui Duan, Kai Ni +1Compute-in-Memory Efficient Transformer Inference
The Pennsylvania State University, Department of Electrical Engineering, University Park, PA, USA · University of Notre Dame, Department of Electrical Engineering, Notre Dame, IN, USA
CADENCE: Context-Adaptive Depth Estimation for Navigation and Computational Efficiency
Apr 8, 2026Timothy K Johnsen, Marco LevoratoPower-Efficient Inference Adaptive Computation
Computational Science joint program with San Diego State University and The University of California, Irvine in CA, USA · Computer Science department at the University of California, Irvine in CA, USA
Splats under Pressure: Exploring Performance-Energy Trade-offs in Real-Time 3D Gaussian Splatting under Constrained GPU Budgets
Apr 8, 2026Muhammad Fahim Tajwar, Arthur Wuhrlin, Bhojan Anand3D Gaussian Splatting 3DGS Acceleration
National University of Singapore · Singapore · Telecom Paris
Critical Patch-Aware Sparse Prompting with Decoupled Training for Continual Learning on the Edge
Apr 8, 2026Wonseon Lim, Jaesung Lee, Dae-Won KimContinual Learning On-Device Training
School of Computer Science and Engineering, Chung-Ang University · Department of Artificial Intelligence, Chung-Ang University
CBM-Dual: A 65-nm Fully Connected Chaotic Boltzmann Machine Processor for Dual Function Simulated Annealing and Reservoir Computing
Apr 8, 2026Kanta Yoshioka, Soshi Hirayae, Yuichiro Tanaka +3Energy-Efficient ML Reservoir Computing
Kyushu Institute of Technology, Kitakyushu, Japan · Future University Hakodate, Hakodate, Japan
FM-CAC: Carbon-Aware Control for Battery-Buffered Edge AI via Time-Series Foundation Models
Apr 7, 2026Kang Yang, Walid A. Hanafy, Prashant Shenoy +1Carbon-Aware Computing Energy-Efficient ML
University of California, Los Angeles Los Angeles, USA · University of Massachusetts Amherst Amherst, USA
Probabilistic Tree Inference Enabled by FDSOI Ferroelectric FETs
Apr 6, 2026Pengyu Ren, Xingtian Wang, Boyang Cheng +9AI Accelerator Inference Energy-Efficient ML
University of Notre Dame, Notre Dame, IN 46556, USA · GlobalFoundries Fab1 LLC &Co. KG, Dresden 01109, Germany
Neuromorphic Computing for Low-Power Artificial Intelligence
Apr 6, 2026Keshava Katti, Pratik Chaudhari, Deep JariwalaAnalog Computing Neuromorphic Computing
Electrical & Systems Engineering, University of Pennsylvania, Philadelphia, USA.
Green AI Carbon Optimizer: Carbon-Efficient Training Location Recommendation and Global AI Energy Demand Forecasting
Apr 6, 2026Yuxin Chen, Hao Gao, Chujie ZouEnergy-Efficient ML Carbon-Aware Computing
University of Helsinki, Finland · Independent Researcher
DHFP-PE: Dual-Precision Hybrid Floating Point Processing Element for AI Acceleration
Apr 6, 2026Shubham Kumar, Vijay Pratap Sharma, Vaibhav Neema +1AI Accelerator Inference Energy-Efficient ML
Institute of Engineering and Technology, Devi Ahilya University, Indore, India · NSDCS Research Group, Indian Institute of Technology Indore, India
Perspective: Towards sustainable exploration of chemical spaces with machine learning
Mar 31, 2026Leonardo Medrano Sandonas, David Balcells, Anton Bochkarev +20AI-Assisted Scientific Research Energy-Efficient ML
Institute for Materials Science and Max Bergmann Center of Biomaterials, TUD Dresden University of Technology, 01062, Dresden, Germany · Hylleraas Centre for Quantum Molecular Sciences, Department of Chemistry, University of Oslo, P.O. Box 1033, Blindern, 0315 Oslo, Norway · Interdisciplinary Centre for Advanced Materials Simulation (ICAMS), Ruhr-University Bochum, 44780 Bochum, Germany +23
CARGO: Carbon-Aware Gossip Orchestration in Smart Shipping
Mar 29, 2026Alexandros S. Kalafatelis, Nikolaos Nomikos, Vasileios Nikolakakis +2Carbon-Aware Computing Federated Learning
Department of Ports Management and Shipping, National and Kapodistrian University of Athens, 34400 Euboea, Greece · Laskaridis Shipping Co. Ltd, 14562, Kifisia Greece
MotiMem: Motion-Aware Approximate Memory for Energy-Efficient Neural Perception in Autonomous Vehicles
Mar 28, 2026Haohua Que, Mingkai Liu, Jiayue Xie +5Energy-Efficient ML Memory-Efficient Inference
University of Georgia · Peking University · Tsinghua University +1
Sustainability Is Not Linear: Quantifying Performance, Energy, and Privacy Trade-offs in On-Device Intelligence
Mar 27, 2026Eziyo Ehsani, Luca Giamattei, Ivano Malavolta +1On-Device Language Model Inference Energy-Efficient ML
∗University of Naples Federico II, Naples, Italy · †Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
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.
Efficient Few-Shot Learning for Edge AI via Knowledge Distillation on MobileViT
Mar 27, 2026Shuhei Tsuyuki, Reda Bensaid, Jérémy Morlier +4Few-Shot Image Classification Edge Computing
Research Institute of Electrical Communication, Tohoku University, Japan · Graduate School of Engineering, Tohoku University, Japan · IMT Atlantique, Lab-STICC, UMR CNRS 6285, F-29238 Brest, France
EcoThink: A Green Adaptive Inference Framework for Sustainable and Accessible Agents
Mar 26, 2026Linxiao Li, Zhixiang LuLLM Inference Efficiency Energy-Efficient ML
The University of Sydney Sydney, Australia · University of Liverpool Liverpool, United Kingdom
Energy Efficient Software Hardware CoDesign for Machine Learning: From TinyML to Large Language Models
Mar 24, 2026Mohammad Saleh Vahdatpour, Yanqing ZhangPower-Efficient Inference Energy-Efficient ML
Department of Computer Science Georgia State University Atlanta, GA, USA
A Theoretical Framework for Energy-Aware Gradient Pruning in Federated Learning
Mar 23, 2026Emmanouil M. AthanasakosFederated Learning Energy-Efficient ML
National and Kapodistrian University of Athens, Department of Informatics and Telecommunications, Athens, Greece
SparseDVFS: Sparse-Aware DVFS for Energy-Efficient Edge Inference
Mar 23, 2026Ziyang Zhang, Zheshun Wu, Jie Liu +1Efficient Neural Network Inference Energy-Efficient ML
Politecnico di Milano, Milan, Italy · Harbin Institute of Technology, Shenzhen, China
SPINONet: Scalable Spiking Physics-informed Neural Operator for Computational Mechanics Applications
Mar 23, 2026Shailesh Garg, Luis Mandl, Somdatta Goswami +1Physics-Informed Neural Operators Neural Operators
Department of Applied Mechanics Indian Institute of Technology Delhi Hauz Khas, New Delhi 110016, India. · Institute of Structural Mechanics and Dynamics in Aerospace Engineering Faculty of Aerospace Engineering and Geodesy University of Stuttgart, Stuttgart, Germany. · Experimental Hepatobiliary Surgery Group Department of Hepatobiliary Surgery and Visceral Transplantation University of Leipzig Medical Center, Leipzig, Germany. +3
AE-LLM: Adaptive Efficiency Optimization for Large Language Models
Mar 20, 2026Kaito Tanaka, Masato Ito, Yuji Nishimura +2LLM Inference Efficiency Energy-Efficient ML
SANNO University

Apr 12, 2026J/K move · Enter open · S save

Energy-Efficient Federated Edge Learning For Small-Scale Datasets in Large IoT Networks

Haihui Xie, Wenkun Wen, Shuwu Chen, Zhaogang Shu+1

College of Computer and Information Sciences, Fujian Agriculture and Forestry University, and also with the Engineering Research Center of Smart Sensing and Agricultural Chip Technology, Fujian Province University, Fuzhou 350002, China · R&D Department, Techphant Technologies Company Ltd., Guangzhou 510310, China · School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou 510006, China

Federated Learning Energy-Efficient ML Edge Computing Data-Efficient Learning Online Learning

Open paper AskPDF arXiv

Large-scale Internet of Things (IoT) networks enable intelligent services such as smart cities and autonomous driving, but often face resource constraints. Collecting heterogeneous sensory data, especially in small-scale datasets, is challenging, and independent edge nodes can lead to inefficient resource utilization and reduced learning performance. To address these issues, this paper proposes a collaborative optimization framework for energy-efficient federated edge learning with small-scale datasets. We first derive an expected learning loss to quantify the relationship between the number of training samples and learning objectives. A stochastic online learning algorithm is then designed to adapt to data variations, and a resource optimization problem with a convergence bound is formulated. Finally, an online distributed algorithm efficiently solves large-scale optimization problems with high scalability. Extensive simulations and autonomous navigation case studies with collision avoidance demonstrate that the proposed approach significantly improves learning performance and resource efficiency compared to state-of-the-art benchmarks.