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• Cloud–Edge Collaborative Image Recognition Task Offloading: A Federated Learning–Driven Framework for Training–Inference Collaboration and Resource Optimization
  With the deep integration of cloud-edge collaboration and artificial intelligence, image-based intelligent perception has been widely applied in fields such as healthcare and marine science.However, the traditional "cloud-side training-cloud/edge inference" pa…
  crossref 2025-11-03T03:35:12Z 置信度 0.82 computer and information sciencescomputer sciencecs.lgartificial intelligenceelectrical engineering, electronic engineering, information engineering
• A Fused Inference Design for Pattern-Based Sparse CNN on Edge Devices
  Weight pruning approaches for Convolution Neural Networks (CNN) has been well developed in the past years. Compared with traditional unstructured and structured pruning, the new state-of-the-art sparse convolution pattern (SCP) based pruning uses certain patte…
  crossref Jia Guo, Radu Teodorescu, Gagan Agrawal 2022-01-24T21:08:34Z 置信度 0.79 cs.lgcomputer and information sciencescomputer scienceelectrical engineering, electronic engineering, information engineeringcryptography and security
• Energy-Efficient Computer Systems: RISC-V Extensions for Machine Learning Inference at IoT's Edge Computing
  Over the past few decades, there have been numerous turning points in the massive transformation of computing systems. The limits of instruction-level parallelism (ILP) and the end of Dennard's scaling pushed the semiconductor sector toward multi-core devices,…
  crossref Ben Sujin, Sangeetha M 2026-02-06T06:04:41Z 置信度 0.82 computer scienceengineeringscalabilitycloud computingartificial intelligence
• Deep Reinforcement Learning for Containerized Edge Intelligence Inference Request Processing in IoT Edge Computing
  Edge intelligence (EI) refers to a set of connected systems and devices for artificial intelligence (AI) data collected and learned near the data collection site. The EI model inference phase has been improved through edge caching technologies such as intellig…
  openalex Lionel Nkenyereye, Kang-Jun Baeg, Wan-Young Chung, Kang‐Jun Baeg 等 2023-09-29 置信度 0.82 Computer scienceEdge computingProvisioningInferenceContainer (type theory)
• Towards Efficient Inference and Training of Deep Neural Networks on Edge Devices
  In recent years, the deployment of deep neural networks (DNNs) on mobile and edge devices has enabled a wide array of ubiquitous computing applications. While this paradigm shift brings computation closer to the data source, it also introduces significant syst…
  crossref Kun Wang 2026-06-01T15:54:37Z 置信度 0.82 computer and information sciencescs.lgcomputer scienceedge computingcryptography and security
• AnalogNAS: A Neural Network Design Framework for Accurate Inference with Analog In-Memory Computing
  The advancement of Deep Learning (DL) is driven by efficient Deep Neural Network (DNN) design and new hardware accelerators. Current DNN design is primarily tailored for general-purpose use and deployment on commercially viable platforms. Inference at the edge…
  crossref Hadjer Benmeziane, Corey Lammie, Irem Boybat, Malte Rasch 等 2023-09-01T17:24:43Z 置信度 0.79 computer and information sciencesartificial intelligencecs.lgcomputer sciencecs.ai
• Resilient Inference for Personalized Federated Learning in Edge Computing Environments
  Federated Learning (FL) and Edge Computing (EC) offer innovative approaches to enable distributed learning systems that prioritize data privacy and low-latency communication. Personalized FL (PFL) enhances traditional FL by tailoring models to the needs of eac…
  crossref Ke Xiao, Qiyuan Wang, Christos Anagnostopoulos, Kevin Bryson 2025-12-01T18:24:05Z 置信度 0.82 computer and information sciencescs.aicomputer sciencecs.lgmachine learning
• Energy Efficient and QoS-Aware Model Selection for DNN Inference in Edge Intelligence
  Edge intelligence is about enabling deep learning applications to run on edge platforms, often under strict Quality of Service (QoS) constraints (e.g., deadlines and accuracy). The heterogeneity and limited computational and energy capacities of edge servers n…
  crossref Hajar Siar, Erik Elmroth 2026-01-20T20:37:16Z 置信度 0.82 computer and information sciencescs.lgelectrical engineering, electronic engineering, information engineeringcomputer sciencesignal processing
• microGEMM: An Effective CNN-Based Inference Acceleration for Edge Computing
  Convolutional Neural Networks (CNNs), a widely recognized deep learning algorithm, have been utilized in various domains such as smart cities and healthcare. However, the remarkable performance of CNNs is accompanied by high resource overhead and deployment co…
  crossref Zheng Liu, Wei Chen, Kai Qian, Haodong Lu 等 2023-10-23T13:54:10Z 置信度 0.79 computer sciencecomputer and information sciencesconvolutional neural networkcs.lgcryptography and security
• Evolutionary Feature Reduction and Edge-Optimized CNN Inference for Large-Scale IoT DDoS Detection
  The rapid proliferation of Internet of Things (IoT) deployments has introduced significant security vulnerabilities, particularly due to Distributed Denial-of-Service (DDoS) attacks launched through compromised IoT botnets. Real-time detection of such attacks …
  crossref Pravir Chitre, Premkumar Sivakumar 2026-08-10T11:15:00Z 置信度 0.82 computer and information sciencescomputer scienceelectrical engineering, electronic engineering, information engineeringcs.lgartificial intelligence
• Research on the Application of Rule Inference Technology in Intelligent Edge-Terminal Scenarios
  Currently, the general mode of Edge-Terminal collaboration is that the terminal executes lightweight tasks, the edge side collects data from terminal devices for centralized computing, and the two ends conduct data interaction. Due to the limited computing pow…
  crossref Yuwei Chen, Jiehao Chen, Jie Shi 2025-07-02T17:40:38Z 置信度 0.82 edge computingcloud computingsoftware deploymentcs.dccryptography and security
• Condition Monitoring of Oil-immersed Transformers Using AI Edge Inference for Incipient Fault Prediction: A case study
  The question of transformer dependability and reliability remains a salient feature in determining the stability of the power supply system. Transformers are amongst the most costly and crucial components of the transmission and distribution network. Every yea…
  crossref George Yogo Odongo, Richard Musabe, Damien Hanyurwimfura, Abubakar Diwani 2022-10-03T20:40:12Z 置信度 0.79 computer scienceedge computingcs.cycs.dcartificial neural network
• [SoK] Systematizing Inference Placement For Deep Learning Across Edge And Cloud Platforms: A Multi-Objective Optimization Perspective
  Edge intelligent applications like VR/AR and language model based chatbots have become widespread with the rapid expansion of IoT and mobile devices. However, constrained edge devices often cannot serve the increasingly large and complex deep learning (DL) mod…
  crossref Zongshun Zhang, Ibrahim Matta 2026-01-26T07:03:05Z 置信度 0.82 computer sciencecomputer and information sciencescloud computingedge computingcs.lg
• The Razor's Edge: Symbolic‐Structuralist Archeology and the Expansion of Archeological Inference
  In this two‐part article, Watson summarizes and discusses a number of new themes in the literature on archeological theory with critical emphasis on symbolic‐structural approaches. Fotiadis comments by applying a structuralist analysis to Watson's argument.
  crossref Patty Jo Watson, Michael Fotiadis 2004-11-23T22:32:18Z 置信度 0.73 edge computingq-bio.gnindustry 4.0cs.semathematics education
• SneakPeek: Data-Aware Model Selection and Scheduling for Inference Serving on the Edge
  Modern applications increasingly rely on inference serving systems to provide low-latency insights with a diverse set of machine learning models. Existing systems often utilize resource elasticity to scale with demand. However, many applications cannot rely on…
  crossref Joel Wolfrath, Daniel Frink, Abhishek Chandra 2026-01-13T16:19:00Z 置信度 0.82 cs.lgcs.aicomputer and information sciencescomputer scienceelectrical engineering, electronic engineering, information engineering
• A Flash-Based Reconfigurable QCNN Inference Accelerator for Edge Applications
  This paper presents a novel in-memory flash-based Quantized Convolutional Neural Network (QCNN) inference accelerator Integrated Circuit (IC), which achieves extremely high throughput and extremely low power, energy, and memory requirements. In this manner, ou…
  crossref Kyler R. Scott, Sunil P. Khatri 2026-01-14T20:39:56Z 置信度 0.82 computer and information sciencescs.lgcomputer scienceelectrical engineering, electronic engineering, information engineeringcs.ai
• Adaptive joint configuration optimization for collaborative inference in edge-cloud systems
  crossref Zheming Yang, Wen Ji, Zhi Wang 2024-04-04T01:02:00Z 置信度 0.66
• A likelihood-based framework for demographic inference from genealogical trees
  Abstract The demographic history of a population drives the pattern of genetic variation and is encoded in the gene-genealogical trees of the sampled alleles. However, existing methods to infer demographic history from genetic data tend to use relatively low-d…
  crossref Caoqi Fan, Jordan L. Cahoon, Bryan L. Dinh, Diego Ortega-Del Vecchyo 等 2023-10-13T21:35:12Z 置信度 0.79 biologypopulationartificial intelligencecs.lgpsychology
• Robust Machine Learning Inference from X-Ray Absorption Near Edge Spectra through Featurization
  X-ray absorption spectroscopy (XAS) is a powerful tool for probing local structures, oxidation states, and electronic properties of functional materials. Based on energy proximity to the absorption edge, spectra are categorized into XANES (near-edge) and EXAFS…
  crossref Yiming Chen, Shyue Ping Ong, Maria Chan 2025-11-24T08:00:53Z 置信度 0.82 computer scienceartificial intelligencecomputer and information sciencescs.lgphysical sciences
• CIS: Checkpointed Inference for Data Drift-Resilient Model Serving at Edge Servers
  Small deep learning models deployed at edge servers suffer from decreasing accuracy due to data drift and hence require continual learning, which leads to resource competition with inference execution, decreasing accuracy and latency service-level-objective (S…
  crossref Sudipta Saha Shubha, Haiying Shen, Ganesh Ananthanarayanan 2026-01-13T16:19:00Z 置信度 0.82 computer and information sciencescs.lgcs.aielectrical engineering, electronic engineering, information engineeringsignal processing
• Pruned Graph Neural Networks for Efficient Edge Classification and Fast Inference
  Optimizing Graph Neural Networks (GNNs) inference is important for various applications, including High-Energy Physics experiments, natural language processing, and point cloud analysis. Effective optimization techniques, such as pruning, model reduction, auto…
  crossref Henry Paschke, James Gaboriault-Whitcomb, Carson Zoccole, Alina Lazar 2025-01-16T18:31:23Z 置信度 0.82 computer and information sciencescs.lgcomputer scienceinterpretabilitycs.ai
• Edge Inference with Fully Differentiable Quantized Mixed Precision Neural Networks
  The large computing and memory cost of deep neural networks (DNNs) often precludes their use in resource-constrained devices. Quantizing the parameters and operations to lower bit-precision offers substantial memory and energy savings for neural network infere…
  crossref Clemens J S Schaefer, Siddharth Joshi, Shan Li, Raul Blazquez 2024-04-09T17:36:09Z 置信度 0.82 cs.lgcs.neartificial neural networkconvolutional neural networkcomputer and information sciences
• Quantifying Carbon Emissions of Machine Learning Inference Models Running on Edge IoT Devices
  crossref Aqsa Bano Kaim Khani, Sam Amiri, Luciano Ost 2026-07-14T19:39:06Z 置信度 0.66
• Context-dependent correlations mislead transcriptomic network inference in bulk and single-cell data
  Abstract Background Correlation is the dominant input to co-expression module discovery and miRNA-target inference. Both rely on an implicit assumption: a Pearson coefficient pooled across heterogeneous samples, whether tissues, cancer types, or cell types, es…
  crossref Amir Asiaee, Polina Bombina, Reginald L. McGee, Jake Reed 等 2026-06-29T20:15:21Z 置信度 0.82 medicinebiologypsychologycs.aicomputer and information sciences
• Architecture for Enabling Edge Inference via Model Transfer from Cloud Domain in a Kubernetes Environment
  The current approaches for energy consumption optimisation in buildings are mainly reactive or focus on scheduling of daily/weekly operation modes in heating. Machine Learning (ML)-based advanced control methods have been demonstrated to improve energy efficie…
  crossref Pekka Pääkkönen, Daniel Pakkala, Jussi Kiljander, Roope Sarala 2020-12-29T19:55:25Z 置信度 0.79 computer sciencecomputer and information sciencescs.lgcloud computingcs.dc
• A Reality Check on Inference at Mobile Networks Edge
  Edge computing is considered a key enabler to deploy Artificial Intelligence platforms to provide real-time applications such as AR/VR or cognitive assistance. Previous works show computing capabilities deployed very close to the user can actually reduce the e…
  crossref Alejandro Cartas, Martin Kocour, Aravindh Raman, Ilias Leontiadis 等 2019-03-20T11:51:28Z 置信度 0.79 computer sciencecs.lgcomputer and information sciencesartificial intelligencecloud computing
• Quantization at the Edge: Evaluating Inference Performance and Quality for SLM Driven Conversational Agents in Virtual Worlds
  Quantised small language models (SLMs) deployed at the edge are increasingly important for enabling high performance AI agents in interactive virtual worlds, where low latency operation is key for seamless user experience. This paper investigates the deploymen…
  crossref Louis Nisiotis, Nikita Markov 2026-08-20T19:04:54Z 置信度 0.82 computer sciencecs.aicomputer and information scienceshuman-computer interactioncs.hc
• Live Demonstration: Low-Power and High-Speed Deep FPGA Inference Engines for Weed Classification at the Edge
  In Low-Power and High-Speed Deep FPGA Inference Engines for Weed Classification at the Edge [1] we implemented GPU- and FPGA-accelerated deterministically binarized Deep Neural Networks (DNNs), tailored toward weed species classification for robotic weed contr…
  crossref Corey Lammie, Mostafa Rahimi Azghadi 2020-09-29T13:22:27Z 置信度 0.79 cs.cvcs.lgconvolutional neural networkcs.aics.ne
• Oaci: Online Adaptive Collaborative Inference Among Edge Devices Under Resource-Constrained Conditions
  Under resource-constrained conditions, the performance level of edge devices, including limited device memory and the ability to load models, significantly impacts inference on these devices. Additionally, network fluctuations further exacerbate inference dela…
  crossref Chao Yang, Jie Li, Enran Xie, Yuxing Liu 等 2025-09-26T17:34:55Z 置信度 0.82 computer and information sciencescs.lgelectrical engineering, electronic engineering, information engineeringcs.aiedge computing
• Intelligent Multi-Level Parking Management using Computer Vision, Edge Inference, and Reinforcement Learning for Slot Assignment
  The rapid expansion of urban transportation systems has intensified the demand for efficient and intelligent parking management solutions. Conventional parking approaches, which primarily depend on manual supervision or static sensor-based mechanisms, often fa…
  crossref Saravanan O, Harikarthick G 2026-06-03T10:30:37Z 置信度 0.82 computer sciencecomputer and information sciencesartificial intelligenceelectrical engineering, electronic engineering, information engineeringcloud computing
• Communication resource allocation and multi-DNN inference optimization in edge computing-aided video analytics
  crossref Weiyang Qian, Rodolfo W.L. Coutinho, Azzedine Boukerche 2026-06-22T15:49:36Z 置信度 0.66
• Uncertainty-Gated Split Inference with Online Threshold Adaptation for Edge-IoT Under Dynamic Networks
  crossref Ali Asghari, Delaram Hosseinalizadeh 2026-08-18T19:14:10Z 置信度 0.66
• Efficient Unaligned Memory Access of Tightly Packed Weights for Deep Neural Network Inference on Edge Devices
  The increase in computational power enabled complex problems to be solved by employing new techniques from the field of Artificial Intelligence (AI) based on Deep Neural Networks (DNN) and Deep Learning (DL). A recent trend is to apply these techniques that ha…
  crossref Ciprian Seiculescu 2022-01-06T20:35:08Z 置信度 0.79 computer scienceartificial intelligenceartificial neural networkneuromorphic engineeringcomputer and information sciences
• Low- and Mixed-Precision Inference Accelerators
  crossref Maarten J. Molendijk, Floran A. M. de Putter, Henk Corporaal 2023-09-30T09:01:55Z 置信度 0.64
• Block-edge reduction in MPEG-1 coded images using statistical inference
  A method is proposed in this paper to filter the block-edge artifact which is a common problem in block-based coding mechanisms used in various digital image and video coding standards. In this method, an algorithm for post-filtering is proposed, based on loca…
  crossref S. Suthaharan 2002-11-22T18:04:31Z 置信度 0.75 eess.ivmath.occontrol theorycs.cvalgorithm
• Privacy-Aware Adaptive Model Splitting for Device-Edge Co Inference
  The device-edge co-inference has emerged as a promising enabling technology for edge intelligence, which bal-ances the computational workload and communication overhead through model splitting. However, device-edge co-inference will expose the users' intermedi…
  crossref Guanwu Jiang, Shujun Han, Xiaodong Xu, Xiaofeng Tao 2024-03-21T17:52:58Z 置信度 0.82 computer and information sciencescryptography and securityinformation theorymachine learningelectrical engineering, electronic engineering, information engineering
• Evaluation of Membership Inference Attack Against Federated Learning With Differential Privacy on Edge Devices
  Federated learning (FL) is a distributed deep learning technique in which training parties are split into a server and clients. Each client trains a model with its own training data and only sends the model to the server. Here, the clients do not share trainin…
  crossref Rei Ueda, Tsunato Nakai, Kota Yoshida, Takeshi Fujino 2023-11-16T18:52:44Z 置信度 0.79 computer and information sciencescryptography and securitycs.lgmachine learningstat.ml
• An Implementation of Secure Split Inference for Large Language Models on Resource-Constrained Edge Devices
  Large Language Models (LLMs) are increasingly deployed on edge devices to improve latency, reduce costs, and increase privacy, compared to cloud-only inference. However, resource constraints often require computation to be done on nearby servers, which introdu…
  crossref Nada Hameed, Abu Kamruzzaman 2026-04-16T19:50:24Z 置信度 0.82 computer and information sciencescomputer sciencecryptography and securityscalabilitycs.ai
• Enabling Collaborative and Green Generative AI Inference in Edge Networks
  Recent advances in the diffusion model mark a significant leap in AI-generated image technology while extending its application to the Internet of Things (IoT). However, deploying these models on resource-constrained edge devices presents considerable challeng…
  crossref Meng Tian, Zhicheng Liu, Chao Qiu, Xiaofei Wang 等 2025-03-11T17:30:35Z 置信度 0.82 computer and information sciencescomputer sciencecs.aics.lgelectrical engineering, electronic engineering, information engineering
• MACA: Memory-aware convolution accelerating for CNN inference on edge devices
  Deep learning inference tasks develop towards the edge due to their latency requirements and privacy issues. However, edge devices are limited by their power consumption and size, and generally have limited resources. The convolutional neural networks (CNN) is…
  crossref Chaoxiong Yi, Songlei Jian, Yusong Tan, Yusen Zhang 2024-07-10T17:21:49Z 置信度 0.82 computer sciencecomputer and information sciencesartificial intelligencecs.lgconvolutional neural network
• Failure-Resilient ML Inference at the Edge through Graceful Service Degradation
  With recent innovations in machine learning (ML) technologies, especially deep learning, many IoT applications have increasingly relied on ML models for various tasks, such as classification, detection, and decision-making. Most of these tasks are latency-sens…
  crossref Walid A. Hanafy, Li Wu, Tarek Abdelzaher, Suhas Diggavi 等 2023-12-25T19:37:01Z 置信度 0.79 computer sciencecloud computingedge computingcs.lginternet of things
• Optimal Input-Dependent Edge-Cloud Partitioning for RNN Inference
  Recurrent Neural Networks (RNNs) such as those based on the Long-Short Term Memory (LSTM) architecture are state-of-the-art deep learning models for sequence analysis. Given the complexity of RNN-based inference, IoT devices typically offload this task to a cl…
  crossref Daniele Jahier Pagliari, Roberta Chiaro, Yukai Chen, Enrico Macii 等 2020-01-23T22:15:31Z 置信度 0.79 computer and information sciencescs.lgcomputer scienceartificial neural networkcs.ai
• Energy-Efficient Edge Inference on Multi-Channel Streaming Data in 28nm HKMG FeFET Technology
  We present a system implementing extremely energy-efficient inference on multi-channel biomedical-sensor data. We leverage Ferroelectric FET (FeFET) to perform classification directly on analog sensor signals. We demonstrate: (i) voltage-controlled multi-domai…
  crossref S. Dutta, W. Chakraborty, J. Gomez, K. Ni 等 2019-07-26T00:25:02Z 置信度 0.79 signal processingbrain-computer interfacemotor imageryelectroencephalographycomputer vision and pattern recognition
• Facility-Location-based Model Partitioning for Efficient Inference in Edge Intelligence
  crossref Wenyun Ma, Chenshan Ren, Chunhui Ai 2026-07-02T19:42:44Z 置信度 0.66
• Adaptive Workload Distribution for Accuracy-aware DNN Inference on Collaborative Edge Platforms
  DNN inference can be accelerated by distributing the workload among a cluster of collaborative edge nodes. Heterogeneity among edge devices and accuracy-performance trade-offs of DNN models present a complex exploration space while catering to the inference pe…
  crossref Zain Taufique, Antonio Miele, Pasi Liljeberg, Anil Kanduri 2024-03-25T19:06:53Z 置信度 0.82 computer and information scienceselectrical engineering, electronic engineering, information engineeringcs.lgcs.cvcryptography and security
• Closing the Loop: Link-Aware Dataset Generation and Edge Learning in UAV-Based Inference Aerial Platforms
  crossref Andrea Caruso, Christian Grasso, Giovanni Schembra 2026-07-16T21:48:13Z 置信度 0.66
• A Simple Hybrid Filter Pruning for Efficient Edge Inference
  Convolutional Neural Networks have been extensively used for solving many vision problems. However, due to high memory and computational requirements, deployment of these models on edge devices is limited. Many embedded friendly models such as MobileNet, Shuff…
  crossref S. H. Shabbeer Basha, Sheethal N Gowda, Jayachandra Dakala 2022-04-27T19:50:34Z 置信度 0.79 computer and information sciencescs.lgcomputer sciencecs.aiartificial intelligence
• Portable Neural Inference via WebAssembly on Resource-Constrained Edge Devices: A Case Study on ESP32-CAM
  The increasing demand for artificial intelligence (AI) inference at the edge introduces a significant challenge: achieving true portability across heterogeneous hardware architectures with minimal overhead. This paper presents a modular and low-footprint solut…
  crossref Rafael Costa Braga, Romulo Gonçalves Lins 2025-11-27T18:54:39Z 置信度 0.82 artificial intelligencecomputer and information sciencesconvolutional neural networkcomputer sciencecryptography and security
• SPICE: Structured Pruning for Inference on Constrained Edge Devices
  To address the challenge of deploying large AI models on resource-constrained edge devices, we propose SPICE, a systematic three-stage pipeline for model selection, optimization, and deployment. For a real-world animal ecology task requiring on-device image se…
  crossref Subhransu Das, Jiaming Cheng, Aniruddha Rakshit, Brijesh Soni 等 2026-02-04T20:45:15Z 置信度 0.82 computer and information sciencescs.aielectrical engineering, electronic engineering, information engineeringcs.lgcs.cv
• TinyML Implementation of CNN-Based Gait Analysis for Low-Cost Motorized Prosthetics: A Proof-of-Concept
  Real-time gait analysis is essential for the development of responsive and reliable motorized prosthetics. Deploying advanced deep learning models on resource-constrained embedded systems, however, remains a major challenge. This proof-of-concept study present…
  crossref João Vitor Y. B. Yamashita, João Paulo R. R. Leite, Jeremias B. Machado 2025-10-31T03:33:07Z 置信度 0.82 artificial intelligencecomputer sciencecomputer and information sciencescs.lgmachine learning
• A Delta Recurrent Neural Network Over TinyML Framework for Arithmetic Coding on the Edge
  In this paper, we use IoT devices at the edge to perform the data compression with arithmetic coding algorithm on the Tiny Machine Learning (TinyML) framework. Our Arithmetic Codec combines a deep learning model, i.e., delta recurrent neural network (DRNN), to…
  crossref Bowei Shan, Yan Xia 2025-06-23T13:27:05Z 置信度 0.82 cs.lgcomputer sciencecs.neartificial neural networkcomputer and information sciences
• TinyML Model for Solar Cell Defect Classification Based on Electroluminescence Images
  crossref S. Boubaker, C. Moussaoui, Adel Mellit, M. Benghanem 2025-03-19T20:33:11Z 置信度 0.66
• Securing Insulin Pumps Against Malicious Injections: A TinyML-Based Anomaly Detection Approach
  crossref Khadija Tlemçani, Kebira Azbeg, Leila Fetjah, Ouail Ouchetto 等 2025-10-07T07:54:59Z 置信度 0.66
• TinyML-enabled structural health monitoring for real-time anomaly detection in civil infrastructure
  SHM is an essential requirement to maintain civil infrastructure safety while extending its operational lifespan, including bridges, buildings, and dams. The conventional SHM systems need centralized data processing together with high-power sensors, even thoug…
  crossref Asma Alshuhail, Hanan Abdullah Mengash, Meshari H. Alanazi, Muhammad Kashif Saeed 等 2025-09-05T21:26:37Z 置信度 0.82 computer sciencecomputer and information sciencesengineeringcloud computingelectrical engineering, electronic engineering, information engineering
• Time Series-Based Electrical Device Classification on Edge with TinyML
  crossref Tolga Reis, Ahmet Teoman Naskali 2025-10-01T00:16:22Z 置信度 0.66
• Special Session - Intermittent TinyML: Powering Sustainable Deep Intelligence Without Batteries
  Tiny battery-free devices running deep neural networks (DNNs) embody intermittent TinyML, a paradigm at the intersection of intermittent computing and deep learning, bringing sustainable intelligence to the extreme edge. This paper, as an overview of a special…
  crossref Hashan Roshantha Mendis, Kasim Sinan Yildirim, Marco Zimmerling, Luca Mottola 等 2025-12-05T17:59:20Z 置信度 0.82 computer scienceartificial intelligencecomputer and information sciencescs.lgcs.ai
• Optimizing TinyML Models for Real-Time Environmental Monitoring: A Comparative Study of Compression Techniques
  crossref Shubneet, Anushka Raj Yadav, Partha Chanda, Shiyabrinta Bhowmik 等 2025-10-18T15:03:05Z 置信度 0.66
• A LOW POWER DYNAMIC BITWIDTH-ADAPTIVE MULTIPLY ACCUMULATE UNIT FOR TINYML ACCELERATORS
  With the increasing demand for the deployment of machine learning models on energy-efficient and low-latency devices, TinyML stands out as an efficient solution for enabling intelligence on edge-constrained devices. TinyML workloads often need energy efficient…
  crossref Shyam Perika, Boddu Ajay, Sumanto Kar 2025-09-26T09:13:58Z 置信度 0.82 computer and information sciencesartificial intelligencemachine learningcomputer sciencecryptography and security
• Addressing Limitations of TinyML Approaches for AI-Enabled Ambient Intelligence
  crossref Antoine Bonneau, Frédéric Le Mouël, Fabien Mieyeville 2024-12-31T22:59:59Z 置信度 0.66
• TinyML: Small Models Making a Big Impact
  crossref Ismail Lamaakal, Chaymae Yahyati, Yassine Maleh, Khalid El Makkaoui 等 2026-05-06T22:07:05Z 置信度 0.66
• Development and Prototyping of a Speech-To-Text (STT) Model and Lip Recognition Software for Tiny Machine Learning (TinyML) Systems
  crossref Logan Wong Duran, Mithil Darur, Kok Zuea Tang 2025-05-03T04:57:44Z 置信度 0.66
• Safeguarding IoT consumer devices: Deep learning with TinyML driven real-time anomaly detection for predictive maintenance
  Internet of Things (IoT) security is paramount for enterprises, as it includes several strategies, techniques, actions, and protocols that aim to alleviate the high vulnerability of cutting-edge businesses. IoT consumer devices, from smart home appliances to w…
  crossref Iyad Katib, Emad Albassam, Sanaa A. Sharaf, Mahmoud Ragab 2025-01-27T05:23:42Z 置信度 0.82 computer scienceartificial intelligencecomputer and information sciencescomputer securityinternet of things
• Optimizing TinyML Models for Bird Call Recognition via Multi-objective Bayesian Search and Knowledge Distillation
  crossref Agnivo Ghosh, Soumen Garai, Suman Samui 2026-04-20T11:51:28Z 置信度 0.66
• StreamNet++: Memory-Efficient Streaming TinyML Model Compilation on Microcontrollers
  The rapid growth of on-device artificial intelligence increases the importance of TinyML inference applications. However, the stringent tiny memory space on the microcontroller unit (MCU) raises the grand challenge when deploying deep neural network (DNN) mode…
  crossref Chen-Fong Hsu, Hong-Sheng Zheng, Yu-Yuan Liu, Tsung Tai Yeh 2024-11-29T09:29:47Z 置信度 0.82 computer and information sciencescryptography and securityartificial intelligenceartificial neural networkcs.lg
• TEDA-forecasting: an unsupervised tinyML incremental learning approach for outlier processing and forecasting
  crossref Pedro Andrade, Morsinaldo Medeiros, Marianne Silva, Daniel G. Costa 等 2025-07-02T02:46:47Z 置信度 0.66
• TinyMetaFed: Efficient Federated Meta-learning for TinyML
  crossref Haoyu Ren, Xue Li, Darko Anicic, Thomas A. Runkler 2024-12-31T23:00:33Z 置信度 0.66
• Data Stream Processing for Resource-Constrained TinyML Systems
  crossref Tobiasz Puslecki, Krzysztof Walkowiak 2026-05-09T22:07:42Z 置信度 0.66
• OdorNet: A lightweight odor recognition method for TinyML in handheld electronic noses using spatiotemporal pseudo-images
  crossref Xin Weng, Jun Fu, Jiayu Ye, Ruifen Hu 等 2025-07-24T15:09:14Z 置信度 0.66
• TinyML Approach for Pre-fall Motion Pattern Detection in Older Adults
  crossref Jefferson Sarmiento-Rojas, Angela Maria Torres-Lara, Pedro Antonio-Aya Parra, Jonnier Sebastián Jaramillo-Isaza 等 2025-10-15T11:57:16Z 置信度 0.66
• An IoT-Based Multimodal Wearable Framework for Real-Time Epileptic Seizures Detection Using TinyML
  crossref Yassmine Ben Dhiab, Moez Hizem, Nader Karmous, Mohamed Ould-Elhassen Aoueileyine 等 2025-04-10T14:18:32Z 置信度 0.66
• TinyML-Based Intrusion Detection System for Handling Class Imbalance in IoT-Edge Domain Using Siamese Neural Network on MCU
  crossref Pietro Fusco, Alberto Montefusco, Gennaro Pio Rimoli, Francesco Palmieri 等 2025-04-07T02:37:18Z 置信度 0.66
• Nephrology Review
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  crossref 2012-07-28T10:20:37Z 置信度 0.74 medicineinternal medicineintensive care medicinediseasemeteorite
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• Optimizing battery life: a TinyML approach to lithium-ion battery health monitoring
  <span lang="EN-US">Electrical vehicles (EVs) are crucial nowadays due to their reduction in greenhouse gas emissions, decreasing dependence on remnant fuels, and improving air quality. For EVs, the battery is the heart that determines range, performance,…
  crossref Kamaraj Lalitha Nisha, Vasanth Pradeep, Padmanabhan Puthiyaveedu Krishnankutty Nair, Sreelakshmi Pillai 等 2025-10-14T13:37:56Z 置信度 0.82 batterymaterials scienceenvironmental economicsenergy storageenvironmental science
• Ultra-Low Power Sensor Data Processing in Smart Agriculture Using Attention-Optimized TinyML Models for Edge Microcontrollers
  This study proposes an attention-optimized TinyML framework for ultra-low power sensor data processing tailored to smart agriculture environments, integrating quantization-aware training (QAT), neural architecture search (NAS), and lightweight attention encode…
  crossref Jaafar Hamza Kadhum, Ahmed Dheyaa Radhi, Laith S. Ismail, Mohammed Ghaleb Waheed 等 2025-12-19T18:56:15Z 置信度 0.82 electrical engineering, electronic engineering, information engineeringcomputer and information sciencesartificial intelligencecomputer sciencescalability
• TinyML-Powered Deep Learning for Real-Time Stress Detection and Health Monitoring
  crossref Merouane Mouadili, El Mokhtar En-Naimi, Mohamed Kouissi 2025-10-26T17:01:43Z 置信度 0.66
• TinyML-Based Approach for Dynamic Transmission Power in LoRaWAN Network
  crossref Muhammad Ali Lodhi, Lei Wang, Khalid Ibrahim Qureshi, Khalid Mahmood 2025-02-28T19:48:46Z 置信度 0.66
• TinyML and Federated Learning for Resource-Constrained Medical Devices
  crossref Pietro Fusco, Gennaro Pio Rimoli, Massimo Ficco 2025-01-22T01:53:05Z 置信度 0.66
• Implementing TinyML in Internet of Things devices: A systematic literature review
  crossref Andrés Felipe Solis Pino, Daniel Steven Moran Pizarro, Pablo H. Ruiz, Vanessa Agredo-Delgado 等 2026-01-10T00:38:10Z 置信度 0.66
• SmartBioGas-PIoT: Physics-informed edge AI and TinyML for real-time monitoring and methane rate prediction in anaerobic digesters
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  crossref Faiyaz Khan Sami, Zakia Sultana Nisa, Md. Hasan Imam Bijoy, Fizar Ahmed 等 2026-04-21T23:48:08Z 置信度 0.82 computer sciencecomputer and information scienceselectrical engineering, electronic engineering, information engineeringengineeringartificial intelligence
• TyBox: An Automatic Design and Code Generation Toolbox for TinyML Incremental On-Device Learning
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• Design and Performance Evaluation of an Ultralow-Power Smart IoT Device With Embedded TinyML for Asset Activity Monitoring
  This article proposes a proof-of-concept device to continuously assess the usage of handheld power tools and detect construction working tasks (e.g., different drilling works) along with potential misusages, e.g., drops, with an energy-efficient architecture d…
  crossref Marco Giordano, Nicolas Baumann, Michele Crabolu, Raphael Fischer 等 2022-04-15T15:23:29Z 置信度 0.79 computer sciencecomputer and information sciencesartificial intelligenceelectrical engineering, electronic engineering, information engineeringmaterials science
• Development of Smart Underground Drainage Leak Detection and Localization Using Acoustic and Flow Sensing with TINYML and IOT
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  crossref Mohamed Islah K I, Elezabeth Skaria, Kavitha Issac, Aby Krishnan U 等 2026-03-29T11:06:41Z 置信度 0.82 computer sciencecomputer and information sciencesartificial intelligenceelectrical engineering, electronic engineering, information engineeringinternet of things
• Elements of TinyML on Constrained Resource Hardware
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• Comparative Analysis and Implementation of Energy-Quality Scalable Multiply-Accumulate Architectures for TinyML Applications
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  crossref Kenn Danielle C. Pelayo, Anastacia B. Alvarez, Adelson N. Chua, Fredrick Angelo R. Galapon 等 2025-01-28T18:32:08Z 置信度 0.82 computer sciencecomputer and information sciencesmaterials sciencecloud computingemerging technologies
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• Power Efficient Non-Mechanical Weather Station: Harnessing TinyML for Rainfall Classification and Environmental Sensing
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• Proof of Concept: A TinyML-Based Image Classifier for Detecting Microplastics and Waste in Simulated Marine Environments
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• TinyML-Based Human and Animal Movement Detection in Agriculture Fields in India
  crossref V. Viswanatha, A. C. Ramachandra, Puneet T. Hegde, Vivek Hegde 等 2024-01-06T13:02:23Z 置信度 0.66
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• An Unsupervised TinyML Approach Applied for Pavement Anomalies Detection Under the Internet of Intelligent Vehicles
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• A New NN-Based Approach to In-Sensor PDM-to-PCM Conversion for Ultra TinyML KWS
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