{"total":159,"rows":[{"id":"2106.07597","title":"MLPerf Tiny Benchmark","created":"2021-06-14","cited_by_count":124,"area":null},{"id":"2103.08295","title":"TinyOL: TinyML with Online-Learning on Microcontrollers","created":"2021-03-15","cited_by_count":65,"area":null},{"id":"2106.12379","title":"AC/DC: Alternating Compressed/DeCompressed Training of Deep Neural Networks","created":"2021-06-23","cited_by_count":59,"area":null},{"id":"2211.12905","title":"GhostNetV2: Enhance Cheap Operation with Long-Range Attention","created":"2022-11-23","cited_by_count":44,"area":null},{"id":"2403.19076","title":"Tiny Machine Learning: Progress and Futures","created":"2024-03-28","cited_by_count":40,"area":null},{"id":"2404.07236","title":"Lightweight Deep Learning for Resource-Constrained Environments: A Survey","created":"2024-04-08","cited_by_count":38,"area":null},{"id":"2110.10486","title":"A TinyML Platform for On-Device Continual Learning with Quantized Latent Replays","created":"2021-10-20","cited_by_count":37,"area":null},{"id":"2108.09187","title":"Quantization Backdoors to Deep Learning Commercial Frameworks","created":"2021-08-20","cited_by_count":29,"area":null},{"id":"2205.14550","title":"Machine Learning for Microcontroller-Class Hardware: A Review","created":"2022-05-29","cited_by_count":28,"area":null},{"id":"2104.01454","title":"Few-Shot Keyword Spotting in Any Language","created":"2021-04-03","cited_by_count":27,"area":null},{"id":"2309.11932","title":"A Machine Learning-oriented Survey on Tiny Machine Learning","created":"2023-09-21","cited_by_count":27,"area":null},{"id":"2212.03332","title":"Edge Impulse: An MLOps Platform for Tiny Machine Learning","created":"2022-11-02","cited_by_count":24,"area":null},{"id":"2105.13331","title":"Quantization and Deployment of Deep Neural Networks on Microcontrollers","created":"2021-05-27","cited_by_count":21,"area":null},{"id":"2306.00001","title":"TinyissimoYOLO: A Quantized, Low-Memory Footprint, TinyML Object Detection Network for Low Power Microcontrollers","created":"2023-05-22","cited_by_count":18,"area":null},{"id":"2307.09988","title":"TinyTrain: Resource-Aware Task-Adaptive Sparse Training of DNNs at the Data-Scarce Edge","created":"2023-07-19","cited_by_count":18,"area":null},{"id":"2106.04008","title":"Widening Access to Applied Machine Learning with TinyML","created":"2021-06-07","cited_by_count":17,"area":null},{"id":"2110.08350","title":"Differentiable Network Pruning for Microcontrollers","created":"2021-10-15","cited_by_count":16,"area":null},{"id":"2104.03182","title":"Deep Learning and Traffic Classification: Lessons learned from a commercial-grade dataset with hundreds of encrypted and zero-day applications","created":"2021-04-07","cited_by_count":14,"area":null},{"id":"2104.03042","title":"On-device Federated Learning with Flower","created":"2021-04-07","cited_by_count":13,"area":null},{"id":"2204.00827","title":"Intelligence at the Extreme Edge: A Survey on Reformable TinyML","created":"2022-04-02","cited_by_count":13,"area":null},{"id":"2304.05503","title":"DistHD: A Learner-Aware Dynamic Encoding Method for Hyperdimensional Classification","created":"2023-04-11","cited_by_count":13,"area":null},{"id":"2403.10549","title":"On-Device Domain Learning for Keyword Spotting on Low-Power Extreme Edge Embedded Systems","created":"2024-03-12","cited_by_count":13,"area":null},{"id":"2201.01863","title":"CFU Playground: Full-Stack Open-Source Framework for Tiny Machine Learning (tinyML) Acceleration on FPGAs","created":"2022-01-05","cited_by_count":11,"area":null},{"id":"2303.08226","title":"DeepAxe: A Framework for Exploration of Approximation and Reliability Trade-offs in DNN Accelerators","created":"2023-03-14","cited_by_count":11,"area":null},{"id":"2307.05999","title":"Flexible and Fully Quantized Ultra-Lightweight TinyissimoYOLO for Ultra-Low-Power Edge Systems","created":"2023-07-12","cited_by_count":11,"area":null},{"id":"2311.11420","title":"LifeLearner: Hardware-Aware Meta Continual Learning System for Embedded Computing Platforms","created":"2023-11-19","cited_by_count":11,"area":null},{"id":"2408.04413","title":"Deeploy: Enabling Energy-Efficient Deployment of Small Language Models On Heterogeneous Microcontrollers","created":"2024-08-08","cited_by_count":11,"area":null},{"id":"2311.11883","title":"Efficient Neural Networks for Tiny Machine Learning: A Comprehensive Review","created":"2023-11-20","cited_by_count":9,"area":null},{"id":"2404.16894","title":"On TinyML and Cybersecurity: Electric Vehicle Charging Infrastructure Use Case","created":"2024-04-25","cited_by_count":9,"area":null},{"id":"2104.10645","title":"Measuring what Really Matters: Optimizing Neural Networks for TinyML","created":"2021-04-21","cited_by_count":8,"area":null},{"id":"2310.18384","title":"MicroNAS: Memory and Latency Constrained Hardware-Aware Neural Architecture Search for Time Series Classification on Microcontrollers","created":"2023-10-27","cited_by_count":8,"area":null},{"id":"2205.10369","title":"Energy-efficient Deployment of Deep Learning Applications on Cortex-M based Microcontrollers using Deep Compression","created":"2022-05-20","cited_by_count":7,"area":null},{"id":"2210.04024","title":"Demand Layering for Real-Time DNN Inference with Minimized Memory Usage","created":"2022-10-08","cited_by_count":7,"area":null},{"id":"2508.15008","title":"Neural Network Quantization for Microcontrollers: A Comprehensive Survey of Methods, Platforms, and Applications","created":"2025-08-20","cited_by_count":7,"area":null},{"id":"2105.03371","title":"The Synergy of Complex Event Processing and Tiny Machine Learning in Industrial IoT","created":"2021-05-04","cited_by_count":6,"area":null},{"id":"2110.01107","title":"TinyFedTL: Federated Transfer Learning on Tiny Devices","created":"2021-10-03","cited_by_count":6,"area":null},{"id":"2111.04779","title":"ML-EXray: Visibility into ML Deployment on the Edge","created":"2021-11-08","cited_by_count":6,"area":null},{"id":"2305.14109","title":"Combining Multi-Objective Bayesian Optimization with Reinforcement Learning for TinyML","created":"2023-05-23","cited_by_count":6,"area":null},{"id":"2306.09973","title":"Enhancing Fault Resilience of QNNs by Selective Neuron Splitting","created":"2023-06-16","cited_by_count":6,"area":null},{"id":"2405.08755","title":"Distributed Threat Intelligence at the Edge Devices: A Large Language Model-Driven Approach","created":"2024-05-14","cited_by_count":6,"area":null},{"id":"2410.07447","title":"TinyLidarNet: 2D LiDAR-based End-to-End Deep Learning Model for F1TENTH Autonomous Racing","created":"2024-10-09","cited_by_count":6,"area":null},{"id":"2508.01915","title":"EgoTrigger: Toward Audio-Driven Image Capture for Human Memory Enhancement in All-Day Energy-Efficient Smart Glasses","created":"2025-08-03","cited_by_count":6,"area":null},{"id":"2112.01319","title":"TinyML Platforms Benchmarking","created":"2021-11-30","cited_by_count":5,"area":null},{"id":"2202.09113","title":"How to Manage Tiny Machine Learning at Scale: An Industrial Perspective","created":"2022-02-18","cited_by_count":5,"area":null},{"id":"2206.03266","title":"Machine Learning Sensors","created":"2022-06-07","cited_by_count":5,"area":null},{"id":"2306.08951","title":"MLonMCU: TinyML Benchmarking with Fast Retargeting","created":"2023-06-15","cited_by_count":5,"area":null},{"id":"2405.07601","title":"On-device Online Learning and Semantic Management of TinyML Systems","created":"2024-05-13","cited_by_count":5,"area":null},{"id":"2406.01655","title":"TinySV: Speaker Verification in TinyML with On-device Learning","created":"2024-06-03","cited_by_count":5,"area":null},{"id":"2410.08855","title":"MATCH: Model-Aware TVM-based Compilation for Heterogeneous Edge Devices","created":"2024-10-11","cited_by_count":5,"area":null},{"id":"2411.05873","title":"Poor Man's Training on MCUs: A Memory-Efficient Quantized Back-Propagation-Free Approach","created":"2024-11-07","cited_by_count":5,"area":null},{"id":"2501.06262","title":"Towards smart and adaptive agents for active sensing on edge devices","created":"2025-01-09","cited_by_count":5,"area":null},{"id":"2506.18927","title":"From Tiny Machine Learning to Tiny Deep Learning: A Survey","created":"2025-06-21","cited_by_count":5,"area":null},{"id":"2605.08195","title":"ExecuTorch -- A Unified PyTorch Solution to Run AI Models On-Device","created":"2026-05-05","cited_by_count":5,"area":null},{"id":"2203.03794","title":"YONO: Modeling Multiple Heterogeneous Neural Networks on Microcontrollers","created":"2022-03-08","cited_by_count":4,"area":null},{"id":"2210.09188","title":"Sub-8-bit quantization for on-device speech recognition: a regularization-free approach","created":"2022-10-17","cited_by_count":4,"area":null},{"id":"2301.04954","title":"Reaching the Edge of the Edge: Image Analysis in Space","created":"2023-01-12","cited_by_count":4,"area":null},{"id":"2301.11899","title":"Is TinyML Sustainable? Assessing the Environmental Impacts of Machine Learning on Microcontrollers","created":"2023-01-27","cited_by_count":4,"area":null},{"id":"2304.05201","title":"TinyReptile: TinyML with Federated Meta-Learning","created":"2023-04-11","cited_by_count":4,"area":null},{"id":"2401.09068","title":"DTMM: Deploying TinyML Models on Extremely Weak IoT Devices with Pruning","created":"2024-01-17","cited_by_count":4,"area":null},{"id":"2402.09264","title":"UR2M: Uncertainty and Resource-Aware Event Detection on Microcontrollers","created":"2024-02-14","cited_by_count":4,"area":null},{"id":"2405.19065","title":"xTern: Energy-Efficient Ternary Neural Network Inference on RISC-V-Based Edge Systems","created":"2024-05-29","cited_by_count":4,"area":null},{"id":"2105.03822","title":"RBNN: Memory-Efficient Reconfigurable Deep Binary Neural Network with IP Protection for Internet of Things","created":"2021-05-09","cited_by_count":3,"area":null},{"id":"2201.11628","title":"Early Detection of Network Attacks Using Deep Learning","created":"2022-01-27","cited_by_count":3,"area":null},{"id":"2206.07164","title":"Edge Security: Challenges and Issues","created":"2022-06-14","cited_by_count":3,"area":null},{"id":"2208.11212","title":"DeepPicarMicro: Applying TinyML to Autonomous Cyber Physical Systems","created":"2022-08-23","cited_by_count":3,"area":null},{"id":"2211.17246","title":"Pex: Memory-efficient Microcontroller Deep Learning through Partial Execution","created":"2022-11-30","cited_by_count":3,"area":null},{"id":"2304.04842","title":"Deploying Machine Learning Models to Ahead-of-Time Runtime on Edge Using MicroTVM","created":"2023-04-10","cited_by_count":3,"area":null},{"id":"2304.12829","title":"Improving Robustness Against Adversarial Attacks with Deeply Quantized Neural Networks","created":"2023-04-25","cited_by_count":3,"area":null},{"id":"2307.02140","title":"Towards Open Federated Learning Platforms: Survey and Vision from Technical and Legal Perspectives","created":"2023-07-05","cited_by_count":3,"area":null},{"id":"2312.02739","title":"LExCI: A Framework for Reinforcement Learning with Embedded Systems","created":"2023-12-05","cited_by_count":3,"area":null},{"id":"2409.19432","title":"MicroFlow: An Efficient Rust-Based Inference Engine for TinyML","created":"2024-09-28","cited_by_count":3,"area":null},{"id":"2410.17957","title":"MCUBERT: Memory-Efficient BERT Inference on Commodity Microcontrollers","created":"2024-10-23","cited_by_count":3,"area":null},{"id":"2411.14467","title":"Towards Scalable Insect Monitoring: Ultra-Lightweight CNNs as On-Device Triggers for Insect Camera Traps","created":"2024-11-18","cited_by_count":3,"area":null},{"id":"2501.12420","title":"Consolidating TinyML Lifecycle with Large Language Models: Reality, Illusion, or Opportunity?","created":"2025-01-20","cited_by_count":3,"area":null},{"id":"2509.14388","title":"eIQ Neutron: Redefining Edge-AI Inference with Integrated NPU and Compiler Innovations","created":"2025-09-17","cited_by_count":3,"area":null},{"id":"2602.13496","title":"Future of Edge AI in biodiversity monitoring","created":"2026-02-13","cited_by_count":3,"area":null},{"id":"2111.15481","title":"Energy-Efficient Inference on the Edge Exploiting TinyML Capabilities for UAVs","created":"2021-11-30","cited_by_count":2,"area":null},{"id":"2204.08702","title":"Software Engineering Approaches for TinyML based IoT Embedded Vision: A Systematic Literature Review","created":"2022-04-19","cited_by_count":2,"area":null},{"id":"2205.08347","title":"Landing AI on Networks: An equipment vendor viewpoint on Autonomous Driving Networks","created":"2022-04-26","cited_by_count":2,"area":null},{"id":"2211.04175","title":"Enhancing Efficiency in Multidevice Federated Learning through Data Selection","created":"2022-11-08","cited_by_count":2,"area":null},{"id":"2303.08970","title":"Gated Compression Layers for Efficient Always-On Models","created":"2023-03-15","cited_by_count":2,"area":null},{"id":"2303.17878","title":"Fused Depthwise Tiling for Memory Optimization in TinyML Deep Neural Network Inference","created":"2023-03-31","cited_by_count":2,"area":null},{"id":"2304.05544","title":"MEMA Runtime Framework: Minimizing External Memory Accesses for TinyML on Microcontrollers","created":"2023-04-12","cited_by_count":2,"area":null},{"id":"2307.11904","title":"Model Compression Methods for YOLOv5: A Review","created":"2023-07-21","cited_by_count":2,"area":null},{"id":"2308.12438","title":"Deploying Deep Reinforcement Learning Systems: A Taxonomy of Challenges","created":"2023-08-23","cited_by_count":2,"area":null},{"id":"2309.05686","title":"Temporal Patience: Efficient Adaptive Deep Learning for Embedded Radar Data Processing","created":"2023-09-11","cited_by_count":2,"area":null},{"id":"2311.07163","title":"Enhancing Lightweight Neural Networks for Small Object Detection in IoT Applications","created":"2023-11-13","cited_by_count":2,"area":null},{"id":"2311.07705","title":"Robust and Scalable Hyperdimensional Computing With Brain-Like Neural Adaptations","created":"2023-11-13","cited_by_count":2,"area":null},{"id":"2311.13349","title":"REDS: Resource-Efficient Deep Subnetworks for Dynamic Resource Constraints","created":"2023-11-22","cited_by_count":2,"area":null},{"id":"2501.10547","title":"HyperCam: Low-Power Onboard Computer Vision for IoT Cameras","created":"2025-01-17","cited_by_count":2,"area":null},{"id":"2502.01700","title":"EdgeMark: An Automation and Benchmarking System for Embedded Artificial Intelligence Tools","created":"2025-02-03","cited_by_count":2,"area":null},{"id":"2504.15300","title":"Collaborative Learning of On-Device Small Model and Cloud-Based Large Model: Advances and Future Directions","created":"2025-04-17","cited_by_count":2,"area":null},{"id":"2508.04035","title":"A Comparative Survey of PyTorch vs TensorFlow for Deep Learning: Usability, Performance, and Deployment Trade-offs","created":"2025-08-06","cited_by_count":2,"area":null},{"id":"2603.04134","title":"InstMeter: An Instruction-Level Method to Predict Energy and Latency of DL Model Inference on MCUs","created":"2026-03-04","cited_by_count":2,"area":null},{"id":"2606.16290","title":"An affordable hardware-aware neural architecture search for deploying convolutional neural networks on ultra-low-power computing platforms","created":"2026-06-15","cited_by_count":2,"area":null},{"id":"2202.09297","title":"tinyMAN: Lightweight Energy Manager using Reinforcement Learning for Energy Harvesting Wearable IoT Devices","created":"2022-02-18","cited_by_count":1,"area":null},{"id":"2205.10940","title":"Toward smart composites: small-scale, untethered prediction and control for soft sensor/actuator systems","created":"2022-05-22","cited_by_count":1,"area":null},{"id":"2212.01635","title":"An Empirical Study of AI Techniques in Mobile Applications","created":"2022-12-03","cited_by_count":1,"area":null},{"id":"2303.16100","title":"Energy-efficient Task Adaptation for NLP Edge Inference Leveraging Heterogeneous Memory Architectures","created":"2023-03-25","cited_by_count":1,"area":null},{"id":"2310.00953","title":"A Novel IoT Trust Model Leveraging Fully Distributed Behavioral Fingerprinting and Secure Delegation","created":"2023-10-02","cited_by_count":1,"area":null}],"next_cursor":"100"}