Energy-Efficient ML

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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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Energy-Efficient CNN Acceleration with MSDF Digit-Serial Arithmetic on FPGA
Jun 24, 2026Muhammad Usman, Yousef Sadegheih, Dorit MerhofEnergy-Efficient ML FPGA-Based Neural Network Acceleration
Faculty of Informatics and Data Science, University of Regensburg, 93053 Regensburg, Germany
On the Smallness of the Large Language Models Scaling Exponents
Jun 23, 2026Sauro Succi, Peter V. Coveney, Alex HansenEnergy-Efficient ML Language Model Scaling Laws
Italian Institute of Technology, Viale Regina Elena, 291, 00161, Rome, Italy · PoreLab, Physics Department, Norwegian University of Science and Technology, 7491 Trondheim, Norway · Centre for Computational Science, Chemistry Department, University College of London, 20 Gordon Street, WC1H 0AJ, London, United Kingdom
The Energy Consumption of Transformer Fine-Tuning: A Roofline-Inspired Scaling Model
Jun 22, 2026Mansour Zoubeirou a MayakiEnergy-Efficient ML Transformer
Université Lumière Lyon 2, CNRS, Ecole Centrale de Lyon, INSA Lyon, Universite Claude Bernard Lyon 1, LIRIS, UMR5205, 69007 Lyon, France
Leveraging AutoML for Sustainable Deep Learning: A Multi-Objective HPO Approach on Deep Shift Neural Networks
Jun 22, 2026Leona Hennig, Marius LindauerMulti-Fidelity Optimization Energy-Efficient ML
Institute of Artificial Intelligence Leibniz University Hanover · Institute of Artificial Intelligence Leibniz University Hanover L3S Research Center
Domain Adaptation Under Wireless Network Constraints: When Does It Become Green?
Jun 22, 2026Illyyne Saffar, Aurélie Boisbunon, Shruti BotheUnsupervised Domain Adaptation Energy-Efficient ML
Ericsson · Paris, France · Santa Clara, USA
Does Mixture-of-Experts Actually Help Inference on Consumer and Edge Hardware? An Empirical Study
Jun 19, 2026Alfarizy Alfarizy, Hung Truong Thanh Nguyen, René Richard +2LLM Inference Efficiency On-Device Language Model Inference
Analytics Everywhere Lab, University of New Brunswick, Fredericton, NB, Canada · TSAI Lab, University of New Brunswick, Fredericton, NB, Canada
Low-Energy Reduced RISC-V Instruction Subset Processor for Tsetlin Machine Inference at the Edge
Jun 18, 2026Chanda Gupta, Sanidhya Bhatia, Shaurya Priyadarshi +3Energy-Efficient ML Edge Inference
CoDA Laboratory, Indian Institute of Technology Roorkee, Roorkee, India · Microsystems Research Group, Newcastle University, Newcastle upon Tyne, UK
The Hidden Environmental Cost of Poor Coding Practices in TensorFlow and Keras Applications: A Study on Resource Leaks and Carbon Emissions
Jun 18, 2026Bashar Abdallah, Gustavo Santos, Rola Al Bataineh +2Energy-Efficient ML
Department of Computer Engineering and Software Engineering, Polytechnique Montréal Montréal, Canada · Department of IT and Software Engineering, École de Technologie Supérieure (ETS), Université du Québec Montréal, Canada
Low-Cost Neuromorphic Fall Detection Using Synthetic Event Data and Hybrid SNNs
Jun 17, 2026Guillermo Rojas, Gonzalo Soto, Daniel YungeEvent-Based Vision Human Activity Recognition
School of Electrical Engineering Pontificia Universidad Cat´olica de Valpara´ıso, Chile
Green SARC: Predictive Cost and Carbon Governance for Agentic AI Systems
Jun 14, 2026Gaston BesansonEnergy-Efficient ML Cost-Aware Inference
Universidad Torcuato Di Tella
MADAR: An Address-Free Processor
Jun 14, 2026Mohamed Amine BergachEnergy-Efficient ML Hierarchical Memory
Illumina San Diego, California, USA
Sustainable Face Recognition on Low-Power Devices with VQ-VAE Embeddings
Jun 13, 2026Christos Chronis, Georgios Th. Papadopoulos, Iraklis VarlamisEnergy-Efficient ML Edge Inference
Dep. of Informatics and Telematics Harokopio University of Athens Athens, Greece
Beyond Backbone Backpropagation: A Decoupled Strategy for Efficient Transfer Learning
Jun 13, 2026Daniel Vila-Cruz, Laura Morán-Fernández, Verónica Bolón-CanedoEnergy-Efficient ML Transfer Learning
CITIC, Universidade da Coruña, A Coruña, Spain
CREST: Deployment-Realistic Hardware-in-the-Loop NAS for Embedded Sensing Systems
Jun 12, 2026Joseph Q. Zales, Pragya Sharma, Mani SrivastavaEnergy-Efficient ML Edge Inference
University of California, Los Angeles · Los Angeles, California, USA
ReSCom: A Reconfigurable Spiking Neural Network Accelerator Using Stochastic Computing
Jun 11, 2026Ali Alipour Fereidani, Mohammad Rasoul Roshanshah, Saeed SafariEfficient Neural Network Inference Energy-Efficient ML
High-Performance Embedded Architecture Laboratory (HiPEAL) School of Electrical and Computer Engineering College of Engineering, University of Tehran Tehran, Iran · High-Performance Embedded Architecture Laboratory (HiPEAL) School of Electrical and Computer Engineering[cs.AR] College of Engineering, University of Tehran Tehran, Iran
Optical Implementation of Equilibrium Propagation Using Spatial Photonic Ising Machines
Jun 11, 2026Dimitri Vanden Abeele, Daniele Veraldi, Davide Pierangeli +2Equilibrium Propagation Energy-Efficient ML
Laboratoire d’Information Quantique C.P. 224, Université Libre de Bruxelles (ULB), Brussels, Belgium · Dipartimento di Fisica, Sapienza Università di Roma, Rome, Italy
Performance Analysis of YOLOv11 and YOLOv8 for Mixed Traffic Object Detection under Adverse Weather Conditions in Developing Countries
Jun 10, 2026Quoc Thuan Nguyen, Ha Anh Vu, Ngo Dang Thanh Ngan +1Energy-Efficient ML Autonomous Driving Perception
FPT University, Ho Chi Minh City, Vietnam
Life Cycle Assessment of Pre-training the Lucie 7B Open-Source Large Language Model on the Jean Zay Supercomputer
Jun 9, 2026Marc Léobet, Pierre-François Lavallée, Jean-Pierre LorréEnergy-Efficient ML LLM Training
Mens Data · CNRS / IDRIS · LINAGORA
LLM-Guided Neural Architecture Search for Robust Co-Design of Physical Neural Networks
Jun 9, 2026Tyler King, Timothee LeleuEnergy-Efficient ML Large Language Model-Guided Optimization
NTT Research · Princeton University · Stanford University
Energy-Efficient On-Device RAG on a Mobile NPU: System Design and Benchmark on Snapdragon X Elite
Jun 9, 2026Zhiyuan Cheng, Longying LaiRetrieval-Augmented Generation AI Accelerator Inference
School of Engineering Stanford University Stanford, CA, USA · Simon Business School University of Rochester Rochester, NY, USA
HydraCIL: Decoupled Class-Incremental Learning through Prototype-Guided Multi-Head Classifiers
Jun 8, 2026Daniel Vila-Cruz, Laura Morán-Fernández, Verónica Bolón-CanedoClass-Incremental Learning Energy-Efficient ML
Universidade da Coruña CITIC A Coruña, Spain
Assessing the Energy and Carbon Emissions of Neural Speaker Verification Model in Training and Inference
Jun 6, 2026Hugo Leguillier, Driss Matrouf, Guillaume Lechien +1Speaker Verification Energy-Efficient ML
Avignon University, LIA, UPR 4128, France · Aday, France
Enhancing AI Interpretability and Safety through Localised Architectures
Jun 6, 2026Ian Seet, Jonas Bozenhard, Simon OstermannEnergy-Efficient ML Interpretable ML
University of Glasgow · Hamburg University of Technology · Saarland University +2
Attention at the Theoretical Minimum: A Mathematics of Arrays Framework for Memory-Optimal Transformer Kernels
Jun 5, 2026Lenore Mullin, Gaetan HainsGPU Kernel Optimization Energy-Efficient ML
University at Albany, SUNY, Albany, NY, USA · LACL, Université Paris-Est Créteil, Créteil, France
Provably Reduced Sample Cost in Prior-Guided Hyperparameter Optimization
Jun 3, 2026Leona Hennig, Jasmin Brandt, Lukas Fehring +3Multi-Fidelity Optimization Multi-Armed Bandits
Institute of Artificial Intelligence Leibniz University Hanover, Germany · University of Bielefeld Bielefeld, Germany · Institute of Artificial Intelligence, Leibniz University Hanover & L3S Research Center Hanover, Germany
QDS-SNN: Energy-efficient Quantum Deeply-Supervised Spiking Neural Network Algorithm for Traffic Sign Recognition
Jun 3, 2026Zhiguo Qu, Keqi Li, Le Sun +4Energy-Efficient ML Autonomous Driving Perception
School of Computer Science, School of Software, Nanjing University of Information Science and Technology, Nanjing 210044, China · School of Information, Yunnan University of Finance and Economics, Kunming 650221, China · Qatar Center for Quantum Computing, College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar +1
Exploiting In-Sensor Computing for Energy-Efficient Earth Observation
May 31, 2026Luigi Capogrosso, Pietro Bonazzi, Loris Hoxhaj +1Remote Sensing Image Understanding Energy-Efficient ML
Interdisciplinary Transformation University of Austria · ETH Zurich · University of Verona
Evaluation of ML Resource Utilization Requires Model Life Cycle Assessment
May 31, 2026Jared Fernandez, Clara Na, Yonatan Bisk +2Energy-Efficient ML Responsible AI
Language Technologies Institute, Carnegie Mellon University, Pittsburgh, PA, USA · Department of Civil and Environmental Engineering, Carnegie Mellon University, Pittsburgh, PA, USA · Wilton E. Scott Institute for Energy Innovation, Carnegie Mellon University, Pittsburgh, PA, USA
Ego-METAS: Egocentric online Multimodal Energy-efficient Temporal Action Segmentation benchmark
May 29, 2026Maria Santos-Villafranca, Jesus Bermudez-cameo, Alejandro Perez-Yus +2Temporal Action Segmentation Energy-Efficient ML
University of Zaragoza - I3A · Department of Mathematics and Computer Science, University of Catania
Bandwidth Allocation with Device Partitioning for Federated Learning over Industrial IoT networks
May 29, 2026Kangmin Kim, Jaeyoung SongEnergy-Efficient ML Internet of Things
School of Electrical and Electronics Engineering, Pusan National University, Busan, 46241, Korea

Jun 24, 2026, cs.ARJ/K move · Enter open · S save

Energy-Efficient CNN Acceleration with MSDF Digit-Serial Arithmetic on FPGA

Muhammad Usman, Yousef Sadegheih, Dorit Merhof

Faculty of Informatics and Data Science, University of Regensburg, 93053 Regensburg, Germany

Energy-Efficient ML FPGA-Based Neural Network Acceleration

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This paper presents an energy-efficient hardware acceleration of the convolutional layers in the U-Net architecture for image segmentation, implemented on FPGA. While digit-serial arithmetic, particularly most-significant-digit-first (MSDF) techniques, offers a compact hardware footprint, it suffers from initial latency before producing the first output digit. This delay accumulates in cascaded operations like multiplication followed by addition, where each unit introduces its own startup overhead. To overcome this, we propose a merged multiply-add (MMA) architecture that fuses these operations into a unified pipeline. Instead of incurring separate delays, the MMA introduces a single streamlined latency per iteration, shorter than the combined latency of conventional cascaded units, resulting in enhanced throughput and efficiency. The MMA units are designed to process spatial input depths in parallel, achieving significantly higher performance than both standalone MSDF-based and conventional designs. We evaluate the proposed design using U-Net as a target application. Despite operating at a lower frequency than a CPU, the FPGA-based accelerator achieves up to an order of magnitude higher energy efficiency, delivering up to

15.14

GOPS/W compared to

1.93

GOPS/W for CPU-based inference. The design also shows approximately

9\times

reduction in energy consumption compared to MSDF-based FPGA implementations. These results highlight the efficacy of the merged arithmetic approach for resource-constrained, latency-sensitive edge applications in medical imaging and computer vision.

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