Edge ML

ML: Machine Learning

Follow this topic Ask about this topic

Momentum

7 papers in the last four weeks, against 2 the four weeks before. 0.1% of all new papers.

Jul 13Week of Sep 28

Latest papers 94

All topics

Parameter Efficient Multi-Class Intelligent Scheduling for Multimodal Online Distributed Industrial Anomaly Detection
May 15, 2026Heqiang Wang, Weihong Yang, Zheyuan Yang +4Industrial Anomaly Detection Multimodal Anomaly Detection
Pengcheng Laboratory, Shenzhen, 518066, China · Shenzhen International Graduate School, Tsinghua University, Shenzhen, 518055, China
Perforated Neural Networks for Keyword Spotting
May 15, 2026Vishy Gopal, Aris Ilias Goutis, Ralph Crewe +2Keyword Spotting Edge ML
2Purdue University · 3Renesas Electronics · 1Perforated AI
Two-Valued Symmetric Circulant Matrices: Applications in Deep Learning
May 15, 2026Jayakrishna Amathi, Venkata Prasanth Yanambaka, Saraju P. Mohanty +1Structured Sparsity Edge ML
Department of Computer Science and Engineering University of North Texas Denton, TX, USA. · Division of Computer Science Texas Woman’s University Denton, TX, USA.
Edge-AI-Driven Learning-to-Rank for Decentralized Task Allocation in Circular Smart Manufacturing
May 14, 2026Mohammadhossein Ghahramani, Yan Qiao, Mengchu ZhouLearning to Rank Edge ML
Birmingham City University, UK · Macao Institute of Systems Engineering and Collaborative Laboratory for Intelligent Science and Systems, Macau University of Science and Technology, Macau, China · Helen and John C. Hartmann Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark, NJ 07102, USA
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
Evolving Layer-Specific Scalar Functions for Hardware-Aware Transformer Adaptation
May 13, 2026Kieran Carrigg, Sigur de Vries, Amirhossein Sadough +1Efficient Neural Network Inference Efficient ViTs
Department of Machine Learning and Neural Computing Donders Institute for Brain, Cognition, and Behaviour Thomas van Aquinostraat 4, 6525 GD Nijmegen, The Netherlands
Efficient Sensor Fusion for Gesture Recognition on Resource-Constrained Devices
May 13, 2026Pietro Bartoli, Christian Veronesi, Tommaso Bondini +2Human Activity Recognition Tiny ML
Dipartimento di Elettronica, Informazione e Bioingegneria (DEIB) Politecnico di Milano, Milan, Italy
Efficient Neural Architectures for Real-Time ECG Interpretation on Limited Hardware
May 11, 2026Ashery Mbilinyi, Callum O'Riley, Julia Handra +8Efficient Neural Network Inference Electrocardiogram Classification
Department of Computer Science University of Victoria Victoria, Canada · Department of Electrical and Computer Engineering University of British Columbia Vancouver, Canada · Faculty of Medicine University of British Columbia Vancouver, Canada +4
M $^2$ FedAQI: Multimodal Federated Learning for Air Quality Prediction on Heterogeneous Edge Devices
May 10, 2026Manjil Nepal, Kimsie Phan, Tamoghna Ojha +2Multimodal Federated Learning Air Quality Prediction
Department of Computer Science and Engineering, SRM University-AP, India · Department of Mathematics and Computing, IIT (ISM) Dhanbad, India · Department of Mathematics and the Department of Computer Science, University of Central Florida, USA
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
EdgeFlowerTune: Evaluating Federated LLM Fine-Tuning Under Realistic Edge System Constraints
May 9, 2026Jiaxiang Geng, Yiyi Lu, Lunyu Zhao +3LLM Evaluation LLM Fine-Tuning
Duke Kunshan University · Flower Labs · University of Cambridge
Uncertainty-Guided Edge Learning for Deep Image Regression in Remote Sensing
May 7, 2026Anh Vu Nguyen, Dino Sejdinovic, Tat-Jun ChinRemote Sensing Edge ML
Australian Institute for Machine Learning (AIML), Adelaide University
ExecuTorch -- A Unified PyTorch Solution to Run AI Models On-Device
May 5, 2026Mergen Nachin, Digant Desai, Sicheng Stephen Jia +36Heterogeneous Computing Edge ML
Meta · Work done while at Meta.
Smart Passive Acoustic Monitoring: Embedding a Classifier on AudioMoth Microcontroller
May 5, 2026Louis Lerbourg, Paul Peyret, Juliette Linossier +1Efficient Neural Network Inference Sound Event Detection
Univ. Grenoble Alpes, CEA, List, Grenoble, France · Biophonia, France
Resource Utilization of Differentiable Logic Gate Networks Deployed on FPGAs
May 4, 2026Stephen Wormald, Gilon Kravatsky, Damon Woodard +1Efficient Neural Network Inference Edge ML
Florida Institute of National Security University of Florida Gainesville, Florida, USA
Privacy-Preserving Federated Learning: Integrating Zero-Knowledge Proofs in Scalable Distributed Architectures
May 4, 2026Divya GuptaByzantine-Robust Federated Learning Privacy-Preserving ML
Independent Researcher Hyderabad, India
Robust and Explainable Divide-and-Conquer Learning for Intrusion Detection
May 3, 2026Yan Zhou, Kevin Hamlen, Michael De Lucia +5Explainable Artificial Intelligence Network Intrusion Detection
University of Texas at Dallas, Richardson, TX · DEVCOM, Army Research Laboratory, Adelphi, MD · Department of Computer Science, Virginia Tech, Blacksburg, VA +1
Tempus: A Temporally Scalable Resource-Invariant GEMM Streaming Framework for Versal AI Edge
May 1, 2026M. Grailoo, J. Núñez-YáñezLLM Inference Acceleration FPGA Acceleration
∗Linköping University, Sweden · †Universidad Politecnica de Madrid, Spain
A Comparative Analysis of Machine Learning Models for Intrusion Detection in Intelligent Transport Systems
Apr 30, 2026Zawad Yalmie Sazid, Robert Abbas, Sasa MaricIntelligent Transportation Systems Network Intrusion Detection
Victoria University, Sydney, Australia · University of New South Wales College, Sydney, Australia
Lightweight Distillation of SAM 3 and DINOv3 for Edge-Deployable Individual-Level Livestock Monitoring and Longitudinal Visual Analytics
Apr 29, 2026Haiyu Yang, Miel HostensAgricultural Image Analysis Edge ML
College of Agriculture and Life Sciences, Cornell University, Ithaca, NY 14853 USA
Scaling Mobile Agent Systems: From Capability Density to Collective Intelligence
Apr 29, 2026Bowei HeMulti-Agent Collaboration Edge ML
Mohamed bin Zayed University of Artificial Intelligence
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
BitRL: Reinforcement Learning with 1-bit Quantized Language Models for Resource-Constrained Edge Deployment
Apr 27, 2026Md. Ashiq Ul Islam Sajid, Mohammad Sakib Mahmood, Md. Tareq Hasan +3LLM Quantization RL for Language Models
Department of Computer Science BRAC University Dhaka 1212, Bangladesh · Department of Computer Science Missouri State University Springfield, MO, USA · Department of Computer Science & Engineering Prime University Dhaka, Bangladesh +2
Hardware-Efficient Softmax and Layer Normalization with Guaranteed Normalization for Edge Devices
Apr 26, 2026Dawon Choi, Hana Kim, Ji-Hoon KimEfficient Neural Network Inference Layer Normalization
Dept. of Artificial Intelligence Semiconductor Engineering, Hanyang University, Seoul, Republic of Korea · Dept. of Electronic Engineering, Hanyang University, Seoul, Republic of Korea
A Multiplication-Free Spike-Time Learning Algorithm and its Efficient FPGA Implementation for On-Chip SNN Training
Apr 25, 2026Maryam Mirsadeghi, Mojtaba Mirbagheri, Saeed Reza KheradpishehSpiking Neural Networks Edge ML
Maryam Mirsadeghi, Mojtaba Mirbagheri, Saeed Reza Kheradpisheh
Tessera: Secure, Near-Line-Rate Weight Streaming for UMA Edge Accelerators
Apr 25, 2026Animan NaskarEfficient Neural Network Inference AI Accelerator Inference
Indian Institute of Technology (IIT) Ropar
On-Device Vision Training, Deployment, and Inference on a Thumb-Sized Microcontroller
Apr 24, 2026Jeremy EllisOn-Device Training Tiny ML
High School Robotics Educator, British Columbia, Canada
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
DR-Venus: Towards Frontier Edge-Scale Deep Research Agents with Only 10K Open Data
Apr 21, 2026Venus Team, Sunhao Dai, Yong Deng +10Agentic RL Deep Research Agents
Ant Group
Design Rules for Extreme-Edge Scientific Computing on AI Engines
Apr 21, 2026Zhenghua Ma, G Abarajithan, Dimitrios Danopoulos +3Scientific ML Edge ML
University of California San Diego, La Jolla, CA, USA · European Organization for Nuclear Research (CERN), Geneva, Switzerland

May 15, 2026, cs.LGJ/K move · Enter open · S save

Parameter Efficient Multi-Class Intelligent Scheduling for Multimodal Online Distributed Industrial Anomaly Detection

Heqiang Wang, Weihong Yang, Zheyuan Yang, Jia Zhou+3

Pengcheng Laboratory, Shenzhen, 518066, China · Shenzhen International Graduate School, Tsinghua University, Shenzhen, 518055, China

Industrial Anomaly Detection Multimodal Anomaly Detection Edge ML Parameter-Efficient Fine-Tuning

Open paper AskPDF arXiv

Industrial anomaly detection has attracted significant attention as a fundamental challenge in industrial systems. The rapid advancement of heterogeneous industrial sensors has driven industrial anomaly detection from unimodal to multimodal paradigms. However, existing methods are primarily designed for centralized and offline settings, overlooking the distributed and continuously generated data characteristic of real-world industrial environments. With the advancement of edge intelligence, modern edge devices are increasingly capable of not only data acquisition but also distributed model training, enabling collaborative intelligence across the system. Industrial anomaly detection represents a critical application in this context. Motivated by these challenges, we propose a novel framework termed Multimodal Online Distributed Industrial Anomaly Detection (MODIAD). We first present a comprehensive workflow for MODIAD and then formulate a Multi-class Intelligent Scheduling (MIS) problem to coordinate cross class model updates by balancing data sufficiency and class update frequency. To efficiently solve this problem, we design a Sequential Marginal Gain Greedy (SMG) algorithm that enables effective multi-class training under resource constraints. Furthermore, to improve the computational and communication efficiency during training, we propose an Resource Efficient Class-Wise Low Rank Adaptation (REC-LoRA) strategy, which significantly reduces system overhead while preserving detection performance. Extensive experiments on two representative multimodal industrial anomaly detection datasets, MVTec 3D-AD and Eyecandies demonstrate that the proposed approach achieves superior performance and efficiency under the MODIAD scenario.

Momentum

Related topics

Latest papers 94