Edge Inference

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17 papers in the last four weeks, up 240% on the four weeks before. 0.2% of all new papers.

Jul 13Week of Sep 28

Latest papers 123

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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
SED:Lightweight Saliency prediction for Event-based data via Distillation
Jun 12, 2026Romaric Mazna, Jean Martinet, Michele MagnoEvent-Based Vision Edge Inference
i3S/CNRS, Universit´e Cˆote d’Azur · ETH Zürich
NARRAS: Edge-Triggered Distributed Inference for CSI-Based Localization in Vehicular IoT Networks
Jun 10, 2026Rodrigo Oliver, Ricardo Vazquez Alvarez, Alejandro Lancho +1Edge Inference Internet of Things
Signal Theory and Communications Department, Universidad Carlos III de Madrid, Legan´es, 28911, Spain · Gregorio Mara˜n´on Health Research Institute, Madrid, 28007, Spain · Department of Electrical and Computer Engineering, National Yang-Ming Chiao-Tung University (NYCU), Hsinchu, Taiwan +1
CANS: Accelerating Multiuser Collaborative Edge Inference via Cooperative Autodidactic NeuroSurgeon
Jun 8, 2026Zheshun Wu, Ziyang Zhang, Changyao Lin +2Edge Inference Contextual Bandits
School of Computer Science and Technology, Harbin Institute of Technology Shenzhen, Shenzhen 518055, China · NESLab, Politecnico di Milano, 39-20133 Milano, Italy · School of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China +2
SPARX: Secure and Privacy-Aware Approximate CNN Acceleration with Edge RISC-V SoC
Jun 8, 2026Sonu Kumar, Akash Sankhe, Mukul Lokhande +1Edge Inference Privacy-Preserving ML
Centre for Advanced Electronics · Dept. of Electrical Engineering
Edge-Constrained UAV Small-Object Detection with P2 Enhancement and Quantum-Inspired Lightweight Structure Search
Jun 8, 2026Wuming Lei, Yanbin Gao, Mingyan Sun +2Small Object Detection Edge Inference
East China Jiaotong University, Nanchang, China.
Sigma-Branch: Hierarchical Single-Path Network Reconstruction for Dynamic Inference with Reduced Active Parameters
Jun 7, 2026Kohga Tanaka, Hiroaki NishiEfficient Neural Network Inference Dynamic Neural Networks
Graduate School of Science and Technology, Keio University, Kohoku-ku, Yokohama 223-8522, Japan · Department of System Design, Faculty of Science and Technology, Keio University, Kohoku-ku, Yokohama 223-8522, Japan
LightVesselNet: An Ultra-Lightweight Sub-100K Parameter Network for Retinal Blood Vessel Segmentation
Jun 3, 2026Shadman Sobhan, Farhana JalilEfficient Neural Network Inference Retinal Vessel Segmentation
Department of Electrical & Electronic Engineering, Bangladesh University of Engineering and Technology (BUET), Dhaka, Bangladesh.
Real-Time Industrial Defect Detection on Edge Hardware Using Fine-Tuned YOLOv8: A Systematic Benchmark on the NEU Surface Defect Database and MVTec AD with Automotive & Battery Manufacturing Extensions
Jun 3, 2026Emmanuel Ezeji Somtochukwu, Nitesh RijalEdge Inference Industrial Inspection
Zema AI Labs, Abuja, Nigeria
Characterizing the Impact of NVFP4 Quantization for Low-Power Edge AI Deployment
Jun 3, 2026Ovishake Sen, Venkata Nithin Kamineni, Daniel Lobo +3Efficient Neural Network Inference Edge Inference
Department of Electrical and Computer Engineering, University of Florida, Gainesville, USA
UtVAA: Ultra-tiny Vision Transformer with Affix Attention for Mobile Image Classification
Jun 2, 2026Romiyal George, Sathiyamohan Nishankar, Selvarajah Thuseethan +1Vision Transformer Edge Inference
University of Peradeniya, Sri Lanka · Charles Darwin University, Australia
Tiny Collaborative Inference for Occlusion-Robust Object Detection
Jun 1, 2026Chieh-Tung Cheng, Mustafa Aslanov, Eiman KanjoEdge Inference Efficient Object Detection
Imperial College London, United Kingdom · Nottingham Trent University, United Kingdom
FLAME: Physics-Guided Neural Operators for Onboard Satellite Methane Detection in Hyperspectral Imagery
Jun 1, 2026Junhyuk Heo, Junhwan Park, Sancheol Sim +2Edge Inference Remote Sensing
TelePIX, Seoul, Republic of Korea
Optimizing Latent Representations for Robust Building Damage Assessment Onboard Earth Observation Satellites
May 28, 2026Thomas Goudemant, Benjamin FrancesconiDisaster Damage Assessment Building Damage Assessment
Institut de Recherche Technologique Saint Exup´ery
ESAM++: Efficient Online 3D Perception on the Edge
May 28, 2026Qin Liu, Lavisha Aggarwal, Saptarashmi Bandyopadhyay +4Edge Inference Point Cloud Learning
Stanford University · Google · UC San Diego
NeuroEdge: Real-Time Hand Gesture Recognition with High-Density EMG Using Deep Learning at the Edge
May 28, 2026Peter Chudinov, Zhenyu Lin, Jay Motamarry +3EMG-Based Gesture Recognition Efficient Neural Network Inference
Department of Biology1 · School of Engineering in Computer Engineering2 at San Francisco State University, San Francisco, CA 94132. · College of San Mateo3, CA 94402. +1
Joint Optimization of Training and Inference in Federated Edge Learning via Constrained Multi-Objective Deep Reinforcement Learning
May 25, 2026Zhen Li, Jun Cai, Chao Yang +1Multi-Objective Reinforcement Learning Edge Inference
Department of Electrical and Computer Engineering, Concordia University, Montreal, QC, H3G 1M8, Canada · School of Automation, Guangdong University of Technology, China
SAM3-Assisted Training of Lightweight YOLO Models for Precision Pig Farming
May 25, 2026Marcos Vinicius Mendes Faria, Thiago Borges Pereira, Isabella C. F. S. Condotta +2Edge Inference Pseudo-Labeling
Coordenadoria de Inform´atica, Instituto Federal do Esp´ırito Santo (IFES), Serra, ES · Department of Animal Sciences, University of Illinois at Urbana-Champaign, USA
Profiling-Driven Adaptive Distributed Transformer Inference on Embedded Edge Deployment
May 25, 2026Muhammad Azlan Qazi, Alexandros Iosifidis, Qi ZhangEfficient Transformer Inference Transformer Inference
Aarhus University · Aarhus, Denmark · Tampere University +1
YOLO26 vs. YOLOv8: A Comprehensive Architectural Benchmark of Next-Generation Real-Time Object Detection Models
May 24, 2026Chidera G. Oguine, Kanyifeechukwu J. Oguine, Obiozor M. Oguine +1Edge Inference Object Detection
University of Abuja, Nigeria · Vanderbilt University, USA · University of Notre Dame, USA
Rethinking Continual Anomaly Detection on the Edge: Benchmarking Under Realistic Industrial Conditions
May 22, 2026Chad Weatherly, Sen LinEdge Inference Industrial Anomaly Detection
University of Houston
A Constant-Time Implementation Methodology for Activation Functions on Microcontrollers
May 21, 2026Andrii Tyvodar, Andreas Rechberger, Dirmanto Jap +4Edge Inference Neural Network Activation Functions
Faculty of Informatics and Information Technologies, Slovak University of Technology in Bratislava, Bratislava, Slovakia · Temasek Laboratories, Nanyang Technological University, Singapore · Faculty of Computer Science, Albstadt-Sigmaringen University, Albstadt, Germany +2
Characterizing the Fault Response of the Intel Neural Compute Stick 2 Under Single-Pulse Electromagnetic Fault Injection
May 21, 2026Štefan Kučerák, Jakub Breier, Xiaolu HouEdge Inference
Faculty of Informatics and Information Technologies, Slovak University of Technology, Slovakia · TTControl GmbH, Vienna, Austria · State Key Laboratory of Blockchain and Data Security, Zhejiang University
$E^3$ -Agent: An Executable and Evolving Agent for Resource Management of Edge Generative Inference
May 21, 2026Rui Bao, Yaping Sun, Zhiyong Chen +4LLM Inference Efficiency Edge Inference
Cooperative Medianet Innovation Center, Shanghai Jiao Tong University, Shanghai 200240, China · Department of Broadband Communication, Pengcheng Laboratory, Shenzhen 518000, China · China Mobile Research Institute, Beijing 100053, China
LiteViLNet: Lightweight Vision-LiDAR Fusion Network for Efficient Road Segmentation
May 20, 2026Daojie Peng, Bingtao Wang, Fulong Ma +2Edge Inference Camera-LiDAR Fusion
The Hong Kong University of Science and Technology (Guangzhou) · The Shandong University
E-ReCON: An Energy- and Resource-Efficient Precision-Configurable Sparse nvCIM Macro for Conventional and Spiking Neural Edge Inference
May 20, 2026Ankit Kumar Tenwar, Mukul Lokhande, Santosh Kumar VishvakarmaCompute-in-Memory Edge Inference
Dept. of Electrical Engineering Indian Institute of Technology Indore Simrol 453552, Indore, India
MR2-ByteTrack: CNN and Transformer-based Video Object Detection for AI-augmented Embedded Vision Sensor Nodes
May 14, 2026Luca Bompani, Manuele Rusci, Luca Benini +2Edge Inference Efficient Object Detection
Electrical, Electronic and Information Engineering (DEI), University of Bologna, Italy. · Department of Electrical Engineering (ESAT), KU Leuven, Belgium. · Integrated Systems Laboratory (IIS), ETH Zürich, Switzerland. +1
RAMS: Resource-Adaptive and Detection-Conditioned Model Switching for Embedded Edge Perception
May 14, 2026Kushal Khemani, Evan Leri, George Xu +1Adaptive Model Routing Edge Inference
NEXEDGE Research Lab
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
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

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

Joseph Q. Zales, Pragya Sharma, Mani Srivastava

University of California, Los Angeles · Los Angeles, California, USA

Energy-Efficient ML Edge Inference Hardware-Aware NAS

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Deploying neural networks on low-power microcontrollers (MCUs) requires selecting model architectures under tight memory, latency, and energy constraints. Existing workflows often simplify this process along one or more axes: static proxy costs such as FLOPs or parameters, treating one MCU as representative, and continuous-inference tests instead of deployed sensing schedules. These assumptions can mis-rank Pareto-front candidates, miss infeasible deployments, and obscure schedule-dependent energy. We present CREST (Cross-platform Runtime Evaluation and Search Tool), a deployment-realistic hardware-in-the-loop (HIL) neural architecture search (NAS) framework for MCU sensing systems. CREST keeps the optimizer, HIL measurement boundary, logging, and replay workflow fixed while exposing workload, model family, target backend, schedule, quantization, and scoring policy as configurable axes. This makes deployment effects experimentally separable within one reusable workflow. We evaluate CREST on inertial odometry and audio classification across three Arm Cortex-M targets. For inertial odometry, measured-energy HIL search reduces median per-inference energy by 41.7% versus FLOPs-based selection and 40.8% versus memory-traffic-based selection at similar error. FLOPs-based selection also chooses infeasible deployments on memory-constrained targets. On the STM32 N657 target, continuous-inference and duty-cycled searches produce different Pareto frontiers. For audio classification, the same application-level policy selects different DS-CNN architectures on different boards, and cross-board replay changes deployment cost substantially. Overall, CREST shows that deployment-realistic MCU NAS must jointly optimize model architecture, target platform, runtime schedule, and deployment policy rather than relying only on static proxy costs or continuous-inference measurements.

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