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Heterogeneous Edge Devices

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Edge Computing

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Resource-Constrained Edge Devices

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Nvidia Jetson Orin Nano

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AutoMCU: Feasibility-First MCU Neural Network Customization via LLM-based Multi-Agent Systems
May 20, 2026Penglin Dai, Zijie Zhou, Xincao Xu +3Neural Architecture Search Heterogeneous Edge Devices
Memory-Efficient Partitioned DNN Inference on Resource-Constrained Android Crowds
May 20, 2026Lakshani Manamperi, Disumi Pathirana, Thiwanka Pathirana +2Resource-Constrained Edge Devices Graph Neural Network Inference
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 VishvakarmaHeterogeneous Edge Devices Binary Spikes
SA-Kura: An Energy-Efficient Systolic Array Accelerator for Locally-Coupled Kuramoto Drift in Diffusion Sampling
May 20, 2026Jeongmin Jin, Kyeongwon Lee, Mundo Jeong +2Field-Programmable Gate Arrays Nvidia Jetson Orin Nano
AMAR: Lightweight Attention-Based Multi-User Activity Recognition from Wi-Fi CSI
May 20, 2026Amirhossein Mohammadi, Hina TabassumHuman Activity Recognition Channel State Information
FusionSense: Tri-Stage Near-Sensor Learning for Runtime-Adaptive Multimodal Edge Intelligence
May 19, 2026Sanggeon Yun, Ryozo Masukawa, Minhyoung Na +5Multi-Sensor Fusion Multi-Modal Sensing
Deep Tech to Space: Space Data Centers and AI Revolution at the Edge
May 19, 2026Jonas Weiss, Patricia Sagmeister, Gabriel Maiolini Capez +16Satellite Data Centers
Decentralized Direct Volume Rendering: A Browser-Native GPU Architecture for MRI Digital Twins in Resource-Constrained Settings
May 19, 2026Oserebameh Augustine BeckleyRasterization Resource-Constrained Edge Devices
Closed-Loop Hybrid Digital Twin Platform for Connected and Automated Vehicle Validation
May 19, 2026Kanglong Quan, Zhebing Xia, Linfeng Jiang +4Digital Twin Framework Cognitive Digital Twins
Neuromorphic Control of a Flapping-Wing Robot on Resource-Constrained Hardware
May 19, 2026Rim El Filali, Chenrui Feng, Chao Gao +1Neuromorphic Hardware Neural Control
OmniISR: A Unified Framework for Centralized and Federated Learning via Intermediate Supervision and Regularization
May 19, 2026Wei-Bin Kou, Guangxu Zhu, Ming Tang +4Federated Learning Heterogeneous Edge Devices
Quantized Machine Learning Models for Medical Imaging in Low-Resource Healthcare Settings
May 19, 2026Sumanth Meenan Kanneti, Aryan ShahBrain Tumor Classification Medical Image Classification
Beyond Scaling: Agents Are Heading to the Edge
May 18, 2026Chunlin Tian, Dongqi Cai, Wanru Zhao +1Edge Computing Edge
Quantum Machine Learning-based 6G edge Network: Enabling Adaptive Communication and Model Aggregation
May 18, 2026Wenjing Xiao, Jiatai Yan, Chenglong Shi +5V2X Semantic Communications
Heterogeneous Tasks Offloading in Vehicular Edge Computing: A Federated Meta Deep Reinforcement Learning Approach
May 18, 2026Yaorong Huang, Jingtao Luo, Xuechao WangHeterogeneous Edge Devices Federated Reinforcement Learning
PPAI: Enabling Personalized LLM Agent Interoperability for Collaborative Edge Intelligence
May 18, 2026Zile Wang, Qianli Liu, Kaibin Guo +4Heterogeneous Edge Devices Personalized Large Language Model Agents
See Silhouettes in Motion with Neuromorphic Vision
May 18, 2026Pei Zhang, Shijie Lin, Zhou Ge +2Binarization Vision Sensors
Q-LocalAdam: Memory-Efficient Client-Side Adaptive Optimization for Edge Federated Learning
May 17, 2026Vedant Waykole, Haroon R. LoneFederated Learning Heterogeneous Edge Devices
TSFLora: Token-Compressed Split Fine-Tuning for Wireless Edge Networks
May 17, 2026Xianke Qiang, Zheng Chang, Li Wang +1Heterogeneous Edge Devices Parameter-Efficient Fine-Tuning
Centralized vs Decentralized Federated Learning: A trade-off performance analysis
May 15, 2026Chaimaa Medjadji, Guilain Leduc, Sylvain Kubler +1Federated Learning Heterogeneous Edge Devices
Going Beyond the Edge: Distributed Inference of Transformer Models on Ultra-Low-Power Wireless Devices
May 15, 2026Alexander Gräfe, Ding Huo, Vincent de Bakker +3Resource-Constrained Edge Devices Parallel
TFZ-Tree: An Ultra-Lightweight Waveform Classification Framework for Resource-Constrained Devices
May 15, 2026Hao Wang, Kuang Zhang, Yonggang Chi +3Automatic Modulation Classification Waveforms
Perforated Neural Networks for Keyword Spotting
May 15, 2026Vishy Gopal, Aris Ilias Goutis, Ralph Crewe +2Heterogeneous Edge Devices Edge
Federated Learning of Spiking Neural Networks under Heterogeneous Temporal Resolutions
May 14, 2026Sanja Karilanova, Subhrakanti Dey, Ayça ÖzçelikkaleSpiking Neural Networks Heterogeneous Federated Learning
RAMS: Resource-Adaptive and Detection-Conditioned Model Switching for Embedded Edge Perception
May 14, 2026Kushal Khemani, Evan Leri, George Xu +1Vram Edge
Analog RF Computing: A New Paradigm for Energy-Efficient Edge AI Over MU-MIMO Systems
May 14, 2026Wentao Yu, Vincent W. S. WongHeterogeneous Edge Devices In-Memory Computing
Bridging the Rural Healthcare Gap: A Cascaded Edge-Cloud Architecture for Automated Retinal Screening
May 13, 2026Nishi Doshi, Shrey ShahInterpretable Diabetic Retinopathy Grading Skin Lesion Classification
Graph Neural Networks with Triangle-Based Messages for the Multicut Problem
May 13, 2026Jannik Irmai, Lucas Fabian Naumann, Bjoern AndresMaximum Independent Set Graph Neural Networks
Efficient Sensor Fusion for Gesture Recognition on Resource-Constrained Devices
May 13, 2026Pietro Bartoli, Christian Veronesi, Tommaso Bondini +2Hand Gesture Recognition Multi-Sensor Fusion
INAR-VL: Input-Aware Routing for Edge-Cloud Vision-Language Inference
May 13, 2026Ahmed Šabanović, Paul Joe Maliakel, Ivona BrandićReal-Time Inference Large Vision Language Models
Contextual Bandits for Resource-Constrained Devices using Probabilistic Learning
May 13, 2026Marco Angioli, Kevin Johansson, Antonello Rosato +2Linear Contextual Bandits Resource-Constrained Edge Devices
An Agentic AI Framework with Large Language Models and Chain-of-Thought for UAV-Assisted Logistics Scheduling with Mobile Edge Computing
May 13, 2026Hanwen Zhang, Dusit Niyato, Wei Zhang +2Edge Computing Last Mile
CR^2: Cost-Aware Risk-Controlled Routing for Wireless Device-Edge LLM Inference
May 12, 2026Nan Xue, Shengkang Chen, Zhiyong Chen +4Large Language Model Routing Heterogeneous Edge Devices
Trade-offs in Decentralized Agentic AI Discovery Across the Compute Continuum
May 12, 2026Patrizio Dazzi, Emanuele Carlini, Matteo Mordacchini +1Decentralization Edge Computing
Fed-BAC: Federated Bandit-Guided Additive Clustering in Hierarchical Federated Learning
May 12, 2026Satwat Bashir, Tasos Dagiuklas, Muddesar IqbalHeterogeneous Federated Learning Federated Learning
Native Explainability for Bayesian Confidence Propagation Neural Networks: A Framework for Trusted Brain-Like AI
May 12, 2026Georgios Makridis, Georgios Fatouros, John Soldatos +2Bayesian Neural Networks Explainability
Multi-Agent System Identification with Nonlinear Sheaf Diffusion
May 11, 2026Nivar Anwer, Hans Riess, Matthew HaleMulti-Agent Systems Graph Laplacians
A Resilient Solution for Sewer Overflow Monitoring across Cloud and Edge
May 11, 2026Vipin Singh, Tianheng Ling, Peter Ghaly +3Accurate Flood Prediction Cloud
Agentic Performance at the Edge: Insights from Benchmarking
May 11, 2026Shiqiang Wang, Herbert WoisetschlägerAgentic Tool Use Edge
MicroViTv2: Beyond the FLOPS for Edge Energy-Friendly Vision Transformers
May 11, 2026Novendra Setyawan, Chi-Chia Sun, Mao-Hsiu Hsu +2Self-Supervised Vision Transformers Vision Transformer
GELATO: Generative Entropy- and Lyapunov-based Adaptive Token Offloading for Device-Edge Speculative LLM Inference
May 11, 2026Zengzipeng Tang, Yuxuan Sun, Wei Chen +2LLM Inference Efficiency Speculative Decoding
Optimizing Server Placement for Vertical Federated Learning in Dynamic Edge/Fog Networks
May 10, 2026Su Wang, Mung Chiang, H. Vincent PoorFederated Learning Heterogeneous Edge Devices
M $^2$ FedAQI: Multimodal Federated Learning for Air Quality Prediction on Heterogeneous Edge Devices
May 10, 2026Manjil Nepal, Kimsie Phan, Tamoghna Ojha +2Heterogeneous Edge Devices Federated Learning
Adaptive DNN Partitioning and Offloading in Heterogeneous Edge-Cloud Continuum
May 10, 2026Akuen Akoi Deng, Eimantas Butkus, Alfreds Lapkovskis +1Edge Computing Mobilenetv2
MemPrivacy: Privacy-Preserving Personalized Memory Management for Edge-Cloud Agents
May 10, 2026Yining Chen, Jihao Zhao, Bo Tang +5Edge Computing
Smoothing Out the Edges: Continuous-Time Estimation with Gaussian Process Motion Priors on Factor Graphs
May 9, 2026Connor Holmes, Sven Lilge, Zi Cong Guo +2Factor Graphs State Estimation
A Reconfigurable Multiplier Architecture for Error-Resilient Applications in RISC-V Core
May 9, 2026Pragun Jaswal, L. Hemanth Krishna, B. SrinivasuRisc-V Sparse Deep Neural Network Accelerators
EdgeFlowerTune: Evaluating Federated LLM Fine-Tuning Under Realistic Edge System Constraints
May 9, 2026Jiaxiang Geng, Yiyi Lu, Lunyu Zhao +3Heterogeneous Edge Devices Large Language Model Fine-Tuning
UMEDA: Unified Multi-modal Efficient Data Fusion for Privacy-Preserving Graph Federated Learning via Spectral-Gated Attention and Diffusion-Based Operator Alignment
May 8, 2026Shih-Yu Lai, Hirozumi Yamaguchi, Shang-Tse Chen +2Federated Learning Heterogeneous Edge Devices
Task-Oriented Communication for Human Action Understanding via Edge-Cloud Co-Inference
May 8, 2026Jingyi Liu, Cheng Yuan, Lijun He +2Edge Computing Open-Vocabulary Action Recognition
HARMONY: Bridging the Personalization-Generalization Gap by Mitigating Representation Skew in Heterogeneous Split Federated Learning
May 8, 2026Jiseok Youn, You Rim Choi, Goodsol Lee +3Heterogeneous Federated Learning Federated Learning
Computer Use at the Edge of the Statistical Precipice
May 7, 2026Pierluca D'Oro, Sneha Silwal, William Wong +6Computer-Use Agents Agentic Evaluations
XiYOLO: Energy-Aware Object Detection via Iterative Architecture Search and Scaling
May 7, 2026Tony Tran, Richie R. Suganda, Bin HuYolo26 Heterogeneous Edge Devices
A Rod Flow Model for Adam at the Edge of Stability
May 7, 2026Eric Regis, Sinho ChewiAdam Gradient Descent
PoTAcc: A Pipeline for End-to-End Acceleration of Power-of-Two Quantized DNNs
May 7, 2026Rappy Saha, Jude Haris, Nicolas Bohm Agostini +2Sparse Deep Neural Network Accelerators Accelerator
iPhoneBlur: A Difficulty-Stratified Benchmark for Consumer Device Motion Deblurring
May 7, 2026Abdullah Al Shafi, Kazi Saeed AlamMotion Blur Mobilenetv2
Uncertainty-Guided Edge Learning for Deep Image Regression in Remote Sensing
May 7, 2026Anh Vu Nguyen, Dino Sejdinovic, Tat-Jun ChinRemote Sensing Pixel-Level Prediction
Edge Deep Learning in Computer Vision and Medical Diagnostics: A Comprehensive Survey
May 7, 2026Yiwen Xu, Tariq M. Khan, Yang Song +1Heterogeneous Edge Devices Edge Computing
Hardware-Aware Neural Feature Extraction for Resource-Constrained Devices
May 5, 2026Francesco Tosini, Simone Pedroni, Christian Veronesi +5Resource-Constrained Edge Devices Feature Extraction
CP $^2$ N $^2$ : A Control Plane for Heterogeneous Physical Neural Networks
May 5, 2026Stefan Fischer, Maliheh Hariri, Sebastian OtteEdge Computing Neural Network

May 20, 2026 · cs.LGJ/K move · Enter open · S save

AutoMCU: Feasibility-First MCU Neural Network Customization via LLM-based Multi-Agent Systems

Penglin Dai, Zijie Zhou, Xincao Xu, Junhua Wang+2

School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu 611756, China · Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China, Shenzhen 518110, China · School of Computer Science and Engineering, Northeastern University, Shenyang 110819, China

Deploying neural networks on microcontroller units (MCUs) is critical for edge intelligence but remains challenging due to tight memory, storage, and computation constraints. Existing approaches, such as model compression and hardware-aware neural architecture search (HW-NAS), often depend on proxy metrics, incur high search cost, and do not fully bridge the gap between architecture design and verified deployment. This paper presents AutoMCU, a feasibility-first large language model (LLM)-based multi-agent system for automated neural network customization under MCU constraints. Given natural-language task requirements and hardware specifications, AutoMCU iteratively generates structured architecture candidates, filters infeasible designs through vendor toolchain feedback before training, evaluates feasible models under a controlled protocol, and verifies deployability through backend-grounded deployment analysis. AutoMCU includes two key mechanisms: 1) hardware-in-the-loop architecture generation for early elimination of undeployable candidates under RAM and Flash constraints, and 2) state-isolated multi-agent scheduling for stable coordination of proposal, training, evaluation, and deployment stages. Experiments on CIFAR-10 and CIFAR-100 under strict MCU constraints show that AutoMCU achieves competitive accuracy while reducing customization time to about 1--2 hours, compared with hundreds of GPU hours for representative MCU-oriented HW-NAS baselines. Comparisons with ColabNAS and the LLM-based NAS method GENIUS on NAS-Bench-201 further demonstrate the effectiveness and stability of AutoMCU. Real-device deployments on multiple STM32 microcontrollers validate its practical applicability to MCU-scale edge intelligence.

Neural Architecture Search Heterogeneous Edge Devices Multi-Agent Systems

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