arXiv AI

SEDR-Seq2P: A Lightweight Dilated Residual Sequence-to-Point Network for Multi-Task Industrial NILM

arXiv:2607. 28693v1 Announce Type: cross Abstract: Industrial NILM remains challenging because measurement noise and widespread concurrent machine operation reduce the generalization of models tuned on residential data.

arXiv Machine Learning

Jun 24

EnerInfer: Energy-Aware On-Device LLM Inference

arXiv:2606. 23001v1 Announce Type: cross Abstract: On-device LLM inference is increasingly attractive for privacy-preserving, reliable, and cost-effective deployment, yet its energy and thermal costs remain a critical bottleneck.

By Bohua Zou, Nian Liu, Binqi Sun, Matteo Mascherin, Debayan Roy, Yutao Liu, Yu Peng, Ning Jia, Haibo Chen

llms efficiency safety

arXiv Machine Learning

Jun 16

CREST: Deployment-Realistic Hardware-in-the-Loop NAS for Embedded Sensing Systems

arXiv:2606. 15004v1 Announce Type: cross Abstract: Deploying neural networks on low-power microcontrollers (MCUs) requires selecting model architectures under tight memory, latency, and energy constraints.

By Joseph Q. Zales, Pragya Sharma, Mani Srivastava

efficiency

arXiv Machine Learning

Jul 14

HiFi-LLP: High-Fidelity, Low-Cost Latency Predictors with Confidence for Robust HW-NAS

arXiv:2607. 11746v1 Announce Type: new Abstract: With deep neural networks (DNNs) increasingly deployed on edge devices, hardware (HW)-aware optimization techniques--such as HW-aware compression and HW-aware neural architecture search (HW-NAS)--have become essential.

By Shambhavi Balamuthu Sampath, Behzad Shomali, Nael Fasfous, Moritz Thoma, Judeson Anthony Fernando, Lukas Frickenstein, Pierpaolo Mori, Manoj Rohit Vemparala, Alexander Frickenstein, Walter Stechele

arXiv Machine Learning

Jul 28

On-Device Inference versus Wireless Streaming: Energy-Efficient Multi-Modal Deep Learning for Wearable Cardiovascular Patches

arXiv:2510. 18668v4 Announce Type: replace Abstract: Wearable cardiovascular sensor patches promise continuous, unobtrusive monitoring, but their tight energy, memory, and compute budgets make it unclear whether physiological signals should be analyzed on the device or streamed to the cloud for processing.

By Mustafa Fuad Rifet Ibrahim, Tunc Alkanat, Felix Manthey, Maurice Meijer, Alexander Schlaefer, Peer Stelldinger

multimodal benchmarks

arXiv Machine Learning

Jun 11

Energy Use of AI Inference, Efficiency Pathways, and Test-Time Scaling

arXiv:2509. 20241v2 Announce Type: replace Abstract: As AI inference scales to billions of queries, estimates of per-query energy use are increasingly important for capacity planning, efficiency interventions, and policy.

By Felipe Oviedo, Fiodar Kazhamiaka, Esha Choukse, Allen Kim, Amy Luers, Melanie Nakagawa, Ricardo Bianchini, Juan M. Lavista Ferres

efficiency

arXiv AI

Jul 8

Is Your NPU Ready for LLMs? Dissecting the Hidden Efficiency Bottlenecks in Mobile LLM Inference

arXiv:2607. 05475v1 Announce Type: cross Abstract: Deploying Large Language Models (LLMs) on mobile devices enhances privacy and reduces latency, but is severely bottlenecked by hardware inefficiency.

By Guanyu Cai, Ruiming Tian, Lang Yang, Zhouhong Ren, Jinliang Yuan, Lingkun Li, Jiliang Wang

llms efficiency safety

arXiv Machine Learning

Jun 17

From Compression to Deployment: Real-Time and Energy-Efficient FastGRNN on Ultra-Constrained Microcontrollers

arXiv:2606. 17249v1 Announce Type: cross Abstract: The dominant trajectory of modern machine learning has been to scale up: larger models, larger accelerators, larger memory budgets.

By Emre Can Kizilates

efficiency

arXiv Machine Learning

Jun 30

Harvesting AI Computation at the Edge via Generic Approximation

arXiv:2606. 29518v1 Announce Type: cross Abstract: With the widespread adoption of AI in various IoT scenarios such as smart sensing and processing, AI chips have become a common component at the edge.

By Yihan Wang, Huiru Yan, Luxin Zhang, Long Cheng, Weiwei Chen, Ying Wang, Lei Zhang, Cheng Liu, Huawei Li

arXiv AI

Aug 10

A Picture is Worth a Thousand Tokens: How Vision Language Models Cut AI Energy Costs While Improving Accuracy

arXiv:2608. 07427v1 Announce Type: new Abstract: LLM inference accounts for over 90% of AI operational energy, scaling directly with input token count---a critical inefficiency for telecom network analytics and numerical time-series data analysis (NTSDA), where raw multivariate KPI windows from 4G/5G cell sites expand into thousands of floating-point tokens.

By Bhavika Jalli, Nikhil Korati Prasanna, Jayanta Choudhury

llms fine-tuning multimodal benchmarks

arXiv AI

Jun 16

Frontier: Towards Comprehensive and Accurate LLM Inference Simulation

arXiv:2605. 21312v2 Announce Type: replace-cross Abstract: Modern LLM serving is no longer homogeneous or monolithic.

By Yicheng Feng, Xin Tan, Yangtao Deng, Yimin Jiang, Yibo Zhu, Hong Xu

llms agents reinforcement-learning efficiency benchmarks

arXiv Machine Learning

Aug 5

Design-Time Optimization of Deep Neural Networks for Intermittent Learning on Microcontrollers

arXiv:2608. 03589v1 Announce Type: new Abstract: We present a method for designing deep neural networks (DNNs) for intermittent, energy-autonomous, on-device learning on microcontroller units (MCUs).

By Jakob Schubert, Maximilian Kasper, Maximilian Linke, Benedict Herzog, Mark Deutel, Axel Plinge, Dominik Seuss, Christopher Mutschler

agents

arXiv Machine Learning

Jul 13

EvoLP: Self-Evolving Latency Predictor for Model Compression in Real-Time Edge Systems

arXiv:2607. 09063v1 Announce Type: new Abstract: Edge devices are increasingly utilized for deploying deep learning applications on embedded systems.

By Shuo Huai, Hao Kong, Shiqing Li, Xiangzhong Luo, Ravi Subramaniam, Christian Makaya, Qian Lin, Weichen Liu

efficiency benchmarks

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