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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TIMEGATE: Sustainable Time-Boxed Promotion Gates for Continual ML Adaptation Under Resource Constraints
May 27, 2026Abhijit Chakraborty, Suddhasvatta Das, Yash Shah +2Energy-Efficient ML Continual Learning
School of Computing and Augmented Intelligence, Arizona State University, Tempe, USA
Long Live the Librarian! A Persistent Search Sub-Agent for Energy-Efficient Multi-Agent Software Engineering Systems
May 27, 2026Seunghyuk Cho, Sunghyun Choi, Jaeseung Heo +4Multi-Agent System Optimization Software Engineering Agents
1Graduate School of Artificial Intelligence, POSTECH · Department of Computer Science and Engineering, POSTECH · 1Graduate School of Artificial Intelligence, POSTECH, 2Department of Computer Science and Engineering, POSTECH
A Methodology to Assess Power Modeling in Energy-Aware Federated Learning on Heterogeneous Mobile Devices
May 26, 2026Chaimae Jallouli, Karim Boubouh, Robert BasmadjianEnergy-Efficient ML Heterogeneous Computing
Mohammed VI Polytechnic University, Benguerir, Morocco · Khalifa University, Abu Dhabi, UAE
Greening AI Inference with Accuracy and Latency-aware User Incentives
May 26, 2026Vasilios A. Siris, Adamantia Stamou, George D. Stamoulis +2Mechanism Design Energy-Efficient ML
Department of Informatics, School of Information Sciences and Technology, Athens University of Economics and Business, Greece · Huawei Heisenberg Research Center, Munich, Germany
Personalized Federated Learning by Energy-Efficient UAV Communications
May 24, 2026Shiqian Guo, Jianqing Liu, Beatriz LorenzoEnergy-Efficient ML Client Selection in Federated Learning
Department of Computer Science, North Carolina State University, Raleigh, NC 27606 USA · University of Massachusetts, Amherst, MA 01003 USA
MX-SAFE: Versatile Inference- and Training-Proof Microscaling Format with On-the-Fly Exponent and Mantissa Bit Allocation
May 23, 2026Dahoon Park, Jahyun Koo, Sangwoo Hwang +1Mixed-Precision Quantization AI Accelerator Inference
† School of Electrical Engineering, Korea University, South Korea · ‡ Department of Electrical Engineering and Computer Science, DGIST, South Korea
XWind: A Cross-site Router for Large Language Model Inference Serving at Renewable Energy Farms
May 22, 2026Tella Rajashekhar Reddy, Atharva Deshmukh, Liangcheng Yu +7Energy-Efficient ML LLM Inference Serving
Microsoft
PALS: Power-Aware LLM Serving for Mixture-of-Experts Models
May 20, 2026Can Hankendi, Rana Shahout, Minlan Yu +1LLM Inference Efficiency LLM Serving
Boston University · Electrical and Computer Engineering Department · Boston, MA, USA +3
Spiker-LL: An Energy-Efficient FPGA Accelerator Enabling Adaptive Local Learning in Spiking Neural Networks
May 18, 2026Alessio Caviglia, Filippo Marostica, Alessandro Savino +1On-Device Training Energy-Efficient ML
Control and Computer Engineering Department, Politecnico di Torino, Turin, Italy
Scalable neuromorphic computing from autonomous spiking dynamics in a clockless reconfigurable chip
May 15, 2026Eric Oliveira Gomes, Damien RontaniEnergy-Efficient ML Spiking Neural Networks
LMOPS UR4423 Laboratory, CentraleSup´elec and Universit´e de Lorraine, Metz F-57070, France
GenAI for Energy-Efficient and Interference-Aware Compressed Sensing of GNSS Signals on a Google Edge TPU
May 14, 2026Thorben Wegner, Lucas Heublein, Tobias Feigl +3Variational Autoencoders Energy-Efficient ML
Fraunhofer Institute for Integrated Circuits IIS, 90411 Nürnberg, Germany · Friedrich-Alexander-Universität Erlangen-Nürnberg, 91058 Erlangen, Germany
An Amortized Efficiency Threshold for Comparing Neural and Heuristic Solvers in Combinatorial Optimization
May 14, 2026Sohaib AfifiEnergy-Efficient ML Neural Combinatorial Optimization
Univ. Artois, UR 3926, Laboratoire de Génie Informatique et d’Automatique de l’Artois (LGI2A), F-62400 Béthune, France
Analog RF Computing: A New Paradigm for Energy-Efficient Edge AI Over MU-MIMO Systems
May 14, 2026Wentao Yu, Vincent W. S. WongEnergy-Efficient ML Edge Inference
Department of Electrical and Computer Engineering, The University of British Columbia, Vancouver, BC, V6T 1Z4, Canada
EnergyLens: Predictive Energy-Aware Exploration for Multi-GPU LLM Inference Optimization
May 14, 2026Zhiye Song, Kyungmi Lee, Eun Kyung Lee +3LLM Inference Efficiency Energy-Efficient ML
Massachusetts Institute of Technology Cambridge, MA 02139 · IBM Research Yorktown Heights, NY 10598
Towards Resource-Efficient LLMs: End-to-End Energy Accounting of Distillation Pipelines
May 13, 2026Katherine Lambert, Sasha LuccioniEnergy-Efficient ML Language Model Distillation
University of Toronto · 2Sustainable AI Group
The Illusion of Power Capping in LLM Decode: A Phase-Aware Energy Characterisation Across Attention Architectures
May 12, 2026Bole Ma, Ayesha Afzal, Jan Eitzinger +1LLM Inference Efficiency Language Model Decoding
Erlangen National High Performance Computing Center, Erlangen, Germany · Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
Hardware-Software Co-Design of Scalable, Energy-Efficient Analog Recurrent Computations
May 12, 2026Arthur Fyon, Julien Brandoit, Loris Mendolia +3Energy-Efficient ML Keyword Spotting
University of Liège Liège, Belgium
Improving the Performance and Learning Stability of Parallelizable RNNs Designed for Ultra-Low Power Applications
May 12, 2026Julien Brandoit, Arthur Fyon, Damien Ernst +1Energy-Efficient ML Recurrent Neural Networks
Department of Electrical Engineering and Computer Science, University of Liège, Liège, Belgium.
A Fast and Energy-Efficient Latch-Based Memristive Analog Content-Addressable Memory
May 12, 2026Paul-Philipp Manea, Aishwarya Natarajan, Jim Ignowski +2Compute-in-Memory Energy-Efficient ML
PGI-14, Forschungszentrum Jülich, Jülich, Germany · RWTH Aachen University, Aachen, Germany · Hewlett Packard Enterprise, Fort Collins, CO, USA
MicroViTv2: Beyond the FLOPS for Edge Energy-Friendly Vision Transformers
May 11, 2026Novendra Setyawan, Chi-Chia Sun, Mao-Hsiu Hsu +2Energy-Efficient ML Edge Inference
Department of Electro-Optics, National Formosa University, Taiwan · Department of Electrical Engineering, University of Muhammadiyah Malang, Indonesia · Department of Electrical Engineering, National Taipei University, Taiwan +1
Frequency Matching in Spiking Neural Networks for mmWave Sensing
May 11, 2026Di Yu, Zhenyu Liao, Changze Lv +7Energy-Efficient ML Frequency-Domain Feature Learning
College of Computer Science and Technology, Zhejiang University, Hangzhou, China · School of Computer Science, Fudan University, Shanghai, China · Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China +1
Benchmarking Transformer and xLSTM for Time-Series Forecasting of Heat Consumption
May 10, 2026Marja Wahl, Daniel R. Bayer, Sven Rausch +1Energy-Efficient ML Time Series Forecasting
RAUSCH Technology GmbH, Würzburg, Germany · Modeling and Simulation, University of Würzburg, Würzburg, Germany
Adaptive DNN Partitioning and Offloading in Heterogeneous Edge-Cloud Continuum
May 10, 2026Akuen Akoi Deng, Eimantas Butkus, Alfreds Lapkovskis +1Efficient Neural Network Inference Energy-Efficient ML
Department of Computer and Systems Sciences, Stockholm University, Borgarfjordsgatan 12, Stockholm, Sweden
A Reconfigurable Multiplier Architecture for Error-Resilient Applications in RISC-V Core
May 9, 2026Pragun Jaswal, L. Hemanth Krishna, B. SrinivasuEfficient Neural Network Inference Energy-Efficient ML
Indian Institute of Technology Mandi, Mandi–175005, India
XiYOLO: Energy-Aware Object Detection via Iterative Architecture Search and Scaling
May 7, 2026Tony Tran, Richie R. Suganda, Bin HuEnergy-Efficient ML Edge Inference
Department of Research Computing University of Houston Houston, TX 77479 · Department of Electrical and Computer Engineering University of Houston Houston, TX 77479 · Department of Engineering Technology University of Houston Houston, TX 77479
CARMEN: CORDIC-Accelerated Resource-Efficient Multi-Precision Inference Engine for Deep Learning
May 7, 2026Sonu Kumar, Mukul Lokhande, Santosh Kumar Vishvakarma +1Efficient Neural Network Inference Energy-Efficient ML
Dept. of Electrical Engineering Indian Institute of Technology Indore Simrol 453552, Indore, India · EnICS Labs Bar-Ilan University Ramat Gan, Israel
EULER-ADAS: Energy-Efficient & SIMD-Unified Logarithmic-Posit Engine for Precision-Reconfigurable Approximate ADAS Acceleration
May 7, 2026Mukul Lokhande, Ratko Pilipovic, Omkar Kokane +2Energy-Efficient ML FPGA-Based Neural Network Acceleration
NSDCS Research Group, Dept. of Electrical Engineering, Indian Institute of Technology Indore, Indore, India · Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia · EnICS Labs, Bar-Ilan Univ., Ramat Gan 5290002, Israel
LLMSpace: Carbon Footprint Modeling for Large Language Model Inference on LEO Satellites
May 7, 2026Lei Jiang, Adrian Ildefonso, Daniel Loveless +1Energy-Efficient ML LLM Inference
Indiana University
TRAM: Training Approximate Multiplier Structures for Low-Power AI Accelerators
May 6, 2026Chang Meng, Hanyu Wang, Yuyang Ye +3AI Accelerator Inference Energy-Efficient ML
Eindhoven University of Technology Eindhoven, The Netherlands · University of California, Los Angeles Los Angeles, USA · Chinese University of Hong Kong Hong Kong, China +2
S-AI-Recursive: A Bio-Inspired and Temporal Sparse AI Architecture for Iterative, Introspective, and Energy-Frugal Reasoning
May 5, 2026Said SlaouiEnergy-Efficient ML Sparse Neural Networks
Mohammed V University, Rabat, Morocco

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TIMEGATE: Sustainable Time-Boxed Promotion Gates for Continual ML Adaptation Under Resource Constraints

Abhijit Chakraborty, Suddhasvatta Das, Yash Shah, Vivek Gupta+1

School of Computing and Augmented Intelligence, Arizona State University, Tempe, USA

Energy-Efficient ML Continual Learning

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As machine learning(ML) systems evolve to continual adaptation, each re-training cycle uses compute, annotation, and energy. We introduce TIMEGATE, a policy layer managing adaptation by budgeting time, labeling, training, and evaluation. TIMEGATE emits a metric-availability signal M for partial vs. full-evaluation decisions. We validate: (i) labeling outperforms training by 2.3x on Adult tabular; (ii) it transfers to LLaMA-3.1-8B + QLoRA on SST-2 (accuracy 0.80 to 0.96; M =1 in 35/36 runs); (iii) M is informative, 28-cell sensitivity shows M drops to 0.81 at tight thresholds; (iv) 100-cycle simulation achieves 66% evaluation-compute savings with no silent mis-promotions; (v) 10%-slice evaluation on LLaMA uses 89% less wall-clock and energy on a single H200 (ratios agree to 0.2%).

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