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• Enhancing Security in Connected and Autonomous Vehicles: A Pairing Approach and Machine Learning Integration
  The automotive sector faces escalating security risks due to advances in wireless communication technology. Expanding on our previous research using a sensor pairing technique and machine learning models to evaluate IoT sensor data reliability, this study broa…
  datacite Usman Ahmad, Mu Han, Shahid Mahmood 2024 置信度 0.66 Vehicular Ad Hoc Networks and CommunicationsElectrical and Electronic EngineeringFOS: Electrical engineering, electronic engineering, information engineeringEngineeringPhysical Sciences
• Enhancing TinyML Security: Study of Adversarial Attack Transferability
  The recent strides in artificial intelligence (AI) and machine learning (ML) have propelled the rise of TinyML, a paradigm enabling AI computations at the edge without dependence on cloud connections. While TinyML offers real-time data analysis and swift respo…
  datacite Shah, Parin, Govindarajulu, Yuvaraj, Kulkarni, Pavan, Parmar, Manojkumar 2024 置信度 0.66 Cryptography and Security (cs.CR)Artificial Intelligence (cs.AI)FOS: Computer and information sciencesFOS: Computer and information sciences
• Artificial Intelligence for the Classification of Plastic Waste Utilizing TinyML on Low-Cost Embedded Systems
  BCG's implementation of the economy makes Thailand more environmentally conscious. The consolidation policy encourages consumers to eliminate single-use plastics using the 3Rs. This article introduces a solution to reduce plastic waste drastically using artifi…
  datacite Jutarut Chaoraingern, V. Tipsuwanporn, Arjin Numsomran 2023 置信度 0.66 Real-time Water Quality Monitoring and Aquaculture ManagementWater Science and TechnologyEnvironmental SciencePhysical SciencesDeep Learning in Computer Vision and Image Recognition
• Artificial Intelligence for the Classification of Plastic Waste Utilizing TinyML on Low-Cost Embedded Systems
  BCG's implementation of the economy makes Thailand more environmentally conscious. The consolidation policy encourages consumers to eliminate single-use plastics using the 3Rs. This article introduces a solution to reduce plastic waste drastically using artifi…
  datacite Jutarut Chaoraingern, V. Tipsuwanporn, Arjin Numsomran 2023 置信度 0.66 Real-time Water Quality Monitoring and Aquaculture ManagementWater Science and TechnologyEnvironmental SciencePhysical SciencesDeep Learning in Computer Vision and Image Recognition
• Artificial Intelligence for the Classification of Plastic Waste Utilizing TinyML on Low-Cost Embedded Systems
  BCG's implementation of the economy makes Thailand more environmentally conscious. The consolidation policy encourages consumers to eliminate single-use plastics using the 3Rs. This article introduces a solution to reduce plastic waste drastically using artifi…
  datacite Jutarut Chaoraingern, V. Tipsuwanporn, Arjin Numsomran 2023 置信度 0.66 Real-time Water Quality Monitoring and Aquaculture ManagementWater Science and TechnologyEnvironmental SciencePhysical SciencesDeep Learning in Computer Vision and Image Recognition
• Artificial Intelligence for the Classification of Plastic Waste Utilizing TinyML on Low-Cost Embedded Systems
  BCG's implementation of the economy makes Thailand more environmentally conscious. The consolidation policy encourages consumers to eliminate single-use plastics using the 3Rs. This article introduces a solution to reduce plastic waste drastically using artifi…
  datacite Jutarut Chaoraingern, V. Tipsuwanporn, Arjin Numsomran 2023 置信度 0.66 Real-time Water Quality Monitoring and Aquaculture ManagementWater Science and TechnologyEnvironmental SciencePhysical SciencesDeep Learning in Computer Vision and Image Recognition
• Developing a multi-label tinyML machine learning model for an active and optimized greenhouse microclimate control from multivariate sensed data
  In the uncertainties within which the worldwide food security lies nowadays, the agricultural industry is raising a crucial need for being equipped with the state-of-the-art technologies for a more efficient, climate-resilient and sustainable production. The t…
  datacite Ilham Ihoume, Rachid Tadili, Nora Arbaoui, Mohamed Benchrifa 等 2022 置信度 0.66 Precision Agriculture TechnologiesPlant ScienceAgricultural and Biological SciencesLife SciencesDynamic Modeling of Plant Form and Growth
• Developing a multi-label tinyML machine learning model for an active and optimized greenhouse microclimate control from multivariate sensed data
  In the uncertainties within which the worldwide food security lies nowadays, the agricultural industry is raising a crucial need for being equipped with the state-of-the-art technologies for a more efficient, climate-resilient and sustainable production. The t…
  datacite Ilham Ihoume, Rachid Tadili, Nora Arbaoui, Mohamed Benchrifa 等 2022 置信度 0.66 Precision Agriculture TechnologiesPlant ScienceAgricultural and Biological SciencesLife SciencesDynamic Modeling of Plant Form and Growth
• Intelligent and Efficient IoT Through the Cooperation of TinyML and Edge Computing
  The coordinated integration of heterogeneous TinyML-enabled elements in highly distributed Internet of Things (IoT) environments paves the way for the development of truly intelligent and context-aware applications. In this work, we propose a hierarchical ense…
  datacite Ramón Sánchez-Iborra, Abdeljalil Zoubir, Abderahmane Hamdouchi, Ali Idri 等 2023 置信度 0.66 Internet of Things and Edge ComputingComputer Networks and CommunicationsComputer SciencePhysical SciencesEnergy Consumption in Mobile Devices and Networks
• Intelligent and Efficient IoT Through the Cooperation of TinyML and Edge Computing
  The coordinated integration of heterogeneous TinyML-enabled elements in highly distributed Internet of Things (IoT) environments paves the way for the development of truly intelligent and context-aware applications. In this work, we propose a hierarchical ense…
  datacite Ramón Sánchez-Iborra, Abdeljalil Zoubir, Abderahmane Hamdouchi, Ali Idri 等 2023 置信度 0.66 Internet of Things and Edge ComputingComputer Networks and CommunicationsComputer SciencePhysical SciencesEnergy Consumption in Mobile Devices and Networks
• MLonMCU: TinyML Benchmarking with Fast Retargeting
  While there exist many ways to deploy machine learning models on microcontrollers, it is non-trivial to choose the optimal combination of frameworks and targets for a given application. Thus, automating the end-to-end benchmarking flow is of high relevance now…
  datacite van Kempen, Philipp, Stahl, Rafael, Mueller-Gritschneder, Daniel, Schlichtmann, Ulf 2023 置信度 0.66 Machine Learning (cs.LG)FOS: Computer and information sciencesFOS: Computer and information sciences
• Decoupled Access-Execute enabled DVFS for tinyML deployments on STM32 microcontrollers
  Over the last years the rapid growth Machine Learning (ML) inference applications deployed on the Edge is rapidly increasing. Recent Internet of Things (IoT) devices and microcontrollers (MCUs), become more and more mainstream in everyday activities. In this w…
  datacite Alvanaki, Elisavet Lydia, Katsaragakis, Manolis, Masouros, Dimosthenis, Xydis, Sotirios 等 2024 置信度 0.66 Hardware Architecture (cs.AR)FOS: Computer and information sciencesFOS: Computer and information sciences
• BPLS back propagation layer scheduling
  Machine Learning nimmt zunehmend Platz in unsere Gesellschaft ein. Während neuronale Netze nicht mehr nur in großen Rechenzentren anzutreffen sind, findet man sie immer öfters auch auf kleinen Embedded Devices. Machine Learning ist generell sehr energie- und r…
  datacite Dangl, Stefan 2024 置信度 0.66 machine learningtrainingTinyMLon-device learningoptimization techniques
• Automated TinyML algorithm benchmarking for the paper "TinyML-Based Fall Detection for Connected Personal Mobility Vehicles"
  Automated ML algorithm benchmarking for the paper "TinyML-Based Fall Detection for Connected Personal Mobility Vehicles" - https://doi.org/10.32604/cmc.2022.022610 It also contains curated datasets used in the paper
  datacite Bernal Escobedo, Luis 2024 置信度 0.66
• Automated TinyML algorithm benchmarking for the paper "TinyML-Based Fall Detection for Connected Personal Mobility Vehicles"
  Automated ML algorithm benchmarking for the paper "TinyML-Based Fall Detection for Connected Personal Mobility Vehicles" - https://doi.org/10.32604/cmc.2022.022610 It also contains curated datasets used in the paper
  datacite Bernal Escobedo, Luis 2024 置信度 0.66
• TinyAirNet: TinyML Model Transmission for Energy-efficient Image Retrieval from IoT Devices
  This letter introduces an energy-efficient pull-based data collection framework for Internet of Things (IoT) devices that use Tiny Machine Learning (TinyML) to interpret data queries. A TinyML model is transmitted from the edge server to the IoT devices. The d…
  datacite Shiraishi, Junya, Thorsager, Mathias, Pandey, Shashi Raj, Popovski, Petar 2023 置信度 0.66 Networking and Internet Architecture (cs.NI)Signal Processing (eess.SP)FOS: Computer and information sciencesFOS: Computer and information sciencesFOS: Electrical engineering, electronic engineering, information engineering
• Improved Decision Module Selection for Hierarchical Inference in Resource-Constrained Edge Devices
  The Hierarchical Inference (HI) paradigm employs a tiered processing: the inference from simple data samples are accepted at the end device, while complex data samples are offloaded to the central servers. HI has recently emerged as an effective method for bal…
  datacite Behera, Adarsh Prasad, Morabito, Roberto, Widmer, Joerg, Champati, Jaya Prakash 2024 置信度 0.66 Distributed, Parallel, and Cluster Computing (cs.DC)FOS: Computer and information sciencesFOS: Computer and information sciences
• HTVM: Efficient Neural Network Deployment On Heterogeneous TinyML Platforms
  Optimal deployment of deep neural networks (DNNs) on state-of-the-art Systems-on-Chips (SoCs) is crucial for tiny machine learning (TinyML) at the edge. The complexity of these SoCs makes deployment non-trivial, as they typically contain multiple heterogeneous…
  datacite Van Delm, Josse, Vandersteegen, Maarten, Burrello, Alessio, Sarda, Giuseppe Maria 等 2024 置信度 0.66 Programming Languages (cs.PL)Distributed, Parallel, and Cluster Computing (cs.DC)FOS: Computer and information sciencesFOS: Computer and information sciencesD.3.4
• A 1036 TOp/s/W, 12.2 mW, 2.72 mu J/Inference All Digital TNN Accelerator in 22 nm FDX Technology for TinyML Applications
  2022 IEEE Symposium in Low-Power and High-Speed Chips (COOL CHIPS)
  datacite Scherer, Moritz, Di Mauro, Alfio, Rutishauser, Georg, Fischer, Tim 等 2022 置信度 0.66 VLSIIoTTinyMLMachine LearningTNN
• Measuring what Really Matters: Optimizing Neural Networks for TinyML
  arXiv
  datacite Heim, Lennart, Biri, Andreas, Qu, Zhongnan, Thiele, Lothar 2021 置信度 0.66
• Towards Contactless Elevators with TinyML using CNN-based Person Detection and Keyword Spotting
  This study presents a proof of concept for a contactless elevator operation system aimed at minimizing human intervention while enhancing safety, intelligence, and efficiency. A microcontroller-based edge device executing tiny Machine Learning (tinyML) inferen…
  datacite Pimpalkar, Anway S., Niture, Deeplaxmi V. 2024 置信度 0.66 Human-Computer Interaction (cs.HC)Artificial Intelligence (cs.AI)FOS: Computer and information sciencesFOS: Computer and information sciences
• On-device learning, optimization, efficient deployment and execution of machine learning algorithms on resource-constrained IoT hardware
  Edge analytics refers to the application of data analytics and Machine Learning (ML) algorithms on IoT devices. The concept of edge analytics is gaining popularity due to its ability to perform AI-based analytics at the device level, enabling autonomous decisi…
  datacite Sudharsan, Bharath 2022 置信度 0.66 Science and EngineeringEngineeringElectrical & Electronic EngineeringData ScienceTinyML
• On-device Online Learning and Semantic Management of TinyML Systems
  Recent advances in Tiny Machine Learning (TinyML) empower low-footprint embedded devices for real-time on-device Machine Learning. While many acknowledge the potential benefits of TinyML, its practical implementation presents unique challenges. This study aims…
  datacite Ren, Haoyu, Li, Xue, Anicic, Darko, Runkler, Thomas A. 2024 置信度 0.66 Machine Learning (cs.LG)Artificial Intelligence (cs.AI)Databases (cs.DB)Distributed, Parallel, and Cluster Computing (cs.DC)FOS: Computer and information sciences
• TGTM: TinyML-based Global Tone Mapping for HDR Sensors
  Advanced driver assistance systems (ADAS) relying on multiple cameras are increasingly prevalent in vehicle technology. Yet, conventional imaging sensors struggle to capture clear images in conditions with intense illumination contrast, such as tunnel exits, d…
  datacite Todorov, Peter, Hartig, Julian, Meyer-Siemon, Jan, Fiedler, Martin 等 2024 置信度 0.66 Computer Vision and Pattern Recognition (cs.CV)Image and Video Processing (eess.IV)FOS: Computer and information sciencesFOS: Computer and information sciencesFOS: Electrical engineering, electronic engineering, information engineering
• TinyVQA: Compact Multimodal Deep Neural Network for Visual Question Answering on Resource-Constrained Devices
  Traditional machine learning models often require powerful hardware, making them unsuitable for deployment on resource-limited devices. Tiny Machine Learning (tinyML) has emerged as a promising approach for running machine learning models on these devices, but…
  datacite Rashid, Hasib-Al, Sarkar, Argho, Gangopadhyay, Aryya, Rahnemoonfar, Maryam 等 2024 置信度 0.66
• David and Goliath: An Empirical Evaluation of Attacks and Defenses for QNNs at the Deep Edge
  ML is shifting from the cloud to the edge. Edge computing reduces the surface exposing private data and enables reliable throughput guarantees in real-time applications. Of the panoply of devices deployed at the edge, resource-constrained MCUs, e.g., Arm Corte…
  datacite Costa, Miguel, Pinto, Sandro 2024 置信度 0.66 Machine Learning (cs.LG)Artificial Intelligence (cs.AI)Cryptography and Security (cs.CR)FOS: Computer and information sciencesFOS: Computer and information sciences
• EcoPull: Sustainable IoT Image Retrieval Empowered by TinyML Models
  This paper introduces EcoPull, a sustainable Internet of Things (IoT) framework empowered by tiny machine learning (TinyML) models for fetching images from wireless visual sensor networks. Two types of learnable TinyML models are installed in the IoT devices: …
  datacite Thorsager, Mathias, Croisfelt, Victor, Shiraishi, Junya, Popovski, Petar 2024 置信度 0.66 Networking and Internet Architecture (cs.NI)FOS: Computer and information sciencesFOS: Computer and information sciences
• Lightweight Deep Learning for Resource-Constrained Environments: A Survey
  Over the past decade, the dominance of deep learning has prevailed across various domains of artificial intelligence, including natural language processing, computer vision, and biomedical signal processing. While there have been remarkable improvements in mod…
  datacite Liu, Hou-I, Galindo, Marco, Xie, Hongxia, Wong, Lai-Kuan 等 2024 置信度 0.66 Computer Vision and Pattern Recognition (cs.CV)Machine Learning (cs.LG)FOS: Computer and information sciencesFOS: Computer and information sciences
• Exploring Kernel approximations for TinyML inference acceleration on microcontrollers
  datacite Mentzos, Georgios 2024 置信度 0.66 Προσεγγιστικός ΥπολογισμόςΜικροσκοπική Μηχανική ΜάθησηΣυνελικτικό Νευρωνικό ΔίκτυοΜικροελεγκτέςΠροσαρμοσμένη Σχεδίαση
• Usability and Performance Analysis of Embedded Development Environment for On-device Learning
  This research empirically examines embedded development tools viable for on-device TinyML implementation. The research evaluates various development tools with various abstraction levels on resource-constrained IoT devices, from basic hardware manipulation to …
  datacite Scaffi, Enzo, Bonneau, Antoine, Mouël, Frédéric Le, Mieyeville, Fabien 2024 置信度 0.66 Software Engineering (cs.SE)Artificial Intelligence (cs.AI)Machine Learning (cs.LG)FOS: Computer and information sciencesFOS: Computer and information sciences
• Simulating Battery-Powered TinyML Systems Optimised using Reinforcement Learning in Image-Based Anomaly Detection
  Advances in Tiny Machine Learning (TinyML) have bolstered the creation of smart industry solutions, including smart agriculture, healthcare and smart cities. Whilst related research contributes to enabling TinyML solutions on constrained hardware, there is a n…
  datacite Ping, Jared M., Nixon, Ken J. 2024 置信度 0.66 Machine Learning (cs.LG)FOS: Computer and information sciencesFOS: Computer and information sciences
• Decoupled access-execute and dynamic voltage/frequency scaling optimization for energy efficient tinyML deployments on STM32 MCUs
  datacite Alvanaki, Elisavet-Lydia 2024 置信度 0.66 Νευρωνικά ΔίκτυαEdge ComputingDynamic Voltage Frequency ScalingDecoupled Access-Execute
• TinyVQA: Compact Multimodal Deep Neural Network for Visual Question Answering on Resource-Constrained Devices
  Traditional machine learning models often require powerful hardware, making them unsuitable for deployment on resource-limited devices. Tiny Machine Learning (tinyML) has emerged as a promising approach for running machine learning models on these devices, but…
  datacite Rashid, Hasib-Al, Sarkar, Argho, Gangopadhyay, Aryya, Rahnemoonfar, Maryam 等 2024 置信度 0.66 Computer Vision and Pattern Recognition (cs.CV)Artificial Intelligence (cs.AI)Machine Learning (cs.LG)FOS: Computer and information sciencesFOS: Computer and information sciences
• On-Device Training Under 256KB Memory
  On-device training enables the model to adapt to new data collected from the sensors by fine-tuning a pre-trained model. Users can benefit from customized AI models without having to transfer the data to the cloud, protecting the privacy. However, the training…
  datacite Lin, Ji, Zhu, Ligeng, Chen, Wei-Ming, Wang, Wei-Chen 等 2022 置信度 0.66 Computer Vision and Pattern Recognition (cs.CV)FOS: Computer and information sciencesFOS: Computer and information sciences
• MCUNetV2: Memory-Efficient Patch-based Inference for Tiny Deep Learning
  Tiny deep learning on microcontroller units (MCUs) is challenging due to the limited memory size. We find that the memory bottleneck is due to the imbalanced memory distribution in convolutional neural network (CNN) designs: the first several blocks have an or…
  datacite Lin, Ji, Chen, Wei-Ming, Cai, Han, Gan, Chuang 等 2021 置信度 0.66 Computer Vision and Pattern Recognition (cs.CV)FOS: Computer and information sciencesFOS: Computer and information sciences
• Fusing Multi-sensor Input with State Information on TinyML Brains for Autonomous Nano-drones
  Autonomous nano-drones (~10 cm in diameter), thanks to their ultra-low power TinyML-based brains, are capable of coping with real-world environments. However, due to their simplified sensors and compute units, they are still far from the sense-and-act capabili…
  datacite Crupi, Luca, Cereda, Elia, Palossi, Daniele 2024 置信度 0.66 Robotics (cs.RO)FOS: Computer and information sciencesFOS: Computer and information sciences
• Tiny Machine Learning: Progress and Futures
  Tiny Machine Learning (TinyML) is a new frontier of machine learning. By squeezing deep learning models into billions of IoT devices and microcontrollers (MCUs), we expand the scope of AI applications and enable ubiquitous intelligence. However, TinyML is chal…
  datacite Lin, Ji, Zhu, Ligeng, Chen, Wei-Ming, Wang, Wei-Chen 等 2024 置信度 0.66 Machine Learning (cs.LG)Artificial Intelligence (cs.AI)Computer Vision and Pattern Recognition (cs.CV)FOS: Computer and information sciencesFOS: Computer and information sciences
• MicroHD: An Accuracy-Driven Optimization of Hyperdimensional Computing Algorithms for TinyML systems
  Hyperdimensional computing (HDC) is emerging as a promising AI approach that can effectively target TinyML applications thanks to its lightweight computing and memory requirements. Previous works on HDC showed that limiting the standard 10k dimensions of the h…
  datacite Ponzina, Flavio, Rosing, Tajana 2024 置信度 0.66 Performance (cs.PF)Artificial Intelligence (cs.AI)Machine Learning (cs.LG)Neural and Evolutionary Computing (cs.NE)Optimization and Control (math.OC)
• Tiny Graph Neural Networks for Radio Resource Management
  The surge in demand for efficient radio resource management has necessitated the development of sophisticated yet compact neural network architectures. In this paper, we introduce a novel approach to Graph Neural Networks (GNNs) tailored for radio resource man…
  datacite Ghasemi, Ahmad, Pishro-Nik, Hossein 2024 置信度 0.66 Machine Learning (cs.LG)Networking and Internet Architecture (cs.NI)Signal Processing (eess.SP)FOS: Computer and information sciencesFOS: Computer and information sciences
• Boosting keyword spotting through on-device learnable user speech characteristics
  Keyword spotting systems for always-on TinyML-constrained applications require on-site tuning to boost the accuracy of offline trained classifiers when deployed in unseen inference conditions. Adapting to the speech peculiarities of target users requires many …
  datacite Cioflan, Cristian, Cavigelli, Lukas, Benini, Luca 2024 置信度 0.66 Sound (cs.SD)Machine Learning (cs.LG)Audio and Speech Processing (eess.AS)FOS: Computer and information sciencesFOS: Computer and information sciences
• Scheduled Knowledge Acquisition on Lightweight Vector Symbolic Architectures for Brain-Computer Interfaces
  Brain-Computer interfaces (BCIs) are typically designed to be lightweight and responsive in real-time to provide users timely feedback. Classical feature engineering is computationally efficient but has low accuracy, whereas the recent neural networks (DNNs) i…
  datacite Liu, Yejia, Duan, Shijin, Xu, Xiaolin, Ren, Shaolei 2024 置信度 0.66 Machine Learning (cs.LG)Artificial Intelligence (cs.AI)FOS: Computer and information sciencesFOS: Computer and information sciences
• Implementation and Performance Evaluation of Convolutional Neural Network models for Low-Power Microcontrollers with Constrained Resources
  Recent advancements in machine learning have given rise to TinyML, a field focused on developing efficient, miniature models capable of operating on devices with severe power and computationallimitations. evaluate the performance In this paper, we of TensorFlo…
  datacite Krivokapić, Bogdan, Tomović, Slavica, Radusinović, Igor, Jovanović, Ana 2024 置信度 0.66
• Implementation and Performance Evaluation of Convolutional Neural Network models for Low-Power Microcontrollers with Constrained Resources
  Recent advancements in machine learning have given rise to TinyML, a field focused on developing efficient, miniature models capable of operating on devices with severe power and computationallimitations. evaluate the performance In this paper, we of TensorFlo…
  datacite Krivokapić, Bogdan, Tomović, Slavica, Radusinović, Igor, Jovanović, Ana 2024 置信度 0.66
• CiMNet: Towards Joint Optimization for DNN Architecture and Configuration for Compute-In-Memory Hardware
  With the recent growth in demand for large-scale deep neural networks, compute in-memory (CiM) has come up as a prominent solution to alleviate bandwidth and on-chip interconnect bottlenecks that constrain Von-Neuman architectures. However, the construction of…
  datacite Kundu, Souvik, Sarah, Anthony, Joshi, Vinay, Omer, Om J 等 2024 置信度 0.66 Hardware Architecture (cs.AR)Artificial Intelligence (cs.AI)FOS: Computer and information sciencesFOS: Computer and information sciences
• SpokeN-100: A Cross-Lingual Benchmarking Dataset for The Classification of Spoken Numbers in Different Languages
  Benchmarking plays a pivotal role in assessing and enhancing the performance of compact deep learning models designed for execution on resource-constrained devices, such as microcontrollers. Our study introduces a novel, entirely artificially generated benchma…
  datacite Groh, René, Goes, Nina, Kist, Andreas M. 2024 置信度 0.66 Sound (cs.SD)Artificial Intelligence (cs.AI)Audio and Speech Processing (eess.AS)FOS: Computer and information sciencesFOS: Computer and information sciences
• U-TOE: Universal TinyML On-board Evaluation Toolkit for Low-Power IoT
  Results from the TinyML community demonstrate that, it is possible to execute machine learning models directly on the terminals themselves, even if these are small microcontroller-based devices. However, to date, practitioners in the domain lack convenient all…
  datacite Huang, Zhaolan, Zandberg, Koen, Schleiser, Kaspar, Baccelli, Emmanuel 2023 置信度 0.66 Machine Learning (cs.LG)Performance (cs.PF)FOS: Computer and information sciencesFOS: Computer and information sciences
• Wet TinyML: Chemical Neural Network Using Gene Regulation and Cell Plasticity
  In our earlier work, we introduced the concept of Gene Regulatory Neural Network (GRNN), which utilizes natural neural network-like structures inherent in biological cells to perform computing tasks using chemical inputs. We define this form of chemical-based …
  datacite Somathilaka, Samitha, Ratwatte, Adrian, Balasubramaniam, Sasitharan, Vuran, Mehmet Can 等 2024 置信度 0.66 Neural and Evolutionary Computing (cs.NE)Hardware Architecture (cs.AR)FOS: Computer and information sciencesFOS: Computer and information sciences
• CycloWatt: An Affordable, TinyML-enhanced IoT Device Revolutionizing Cycling Power Metrics
  Cycling power measurement is an indispensable metric with profound implications for cyclists' performance and fitness levels. It empowers riders with real-time feedback, supports precise training regimen planning, mitigates injury risks, and enhances muscular …
  datacite Luder, Victor, Bian, Sizhen, Magno, Michele 2024 置信度 0.66 Networking and Internet Architecture (cs.NI)Signal Processing (eess.SP)FOS: Computer and information sciencesFOS: Computer and information sciencesFOS: Electrical engineering, electronic engineering, information engineering
• Towards a tailored mixed-precision sub-8-bit quantization scheme for Gated Recurrent Units using Genetic Algorithms
  Despite the recent advances in model compression techniques for deep neural networks, deploying such models on ultra-low-power embedded devices still proves challenging. In particular, quantization schemes for Gated Recurrent Units (GRU) are difficult to tune …
  datacite Miccini, Riccardo, Cerioli, Alessandro, Laroche, Clément, Piechowiak, Tobias 等 2024 置信度 0.66 Machine Learning (cs.LG)Neural and Evolutionary Computing (cs.NE)Signal Processing (eess.SP)FOS: Computer and information sciencesFOS: Computer and information sciences
• TinyML ou quand le machine learning rencontre les microcontrôleurs
  TinyML est un nouveau champ d'application du machine learning appliqué aux tous petits objets, ceux fonctionnant avec un microcontrôleur. On s'intéresse en effet ici à des plateformes matérielles ne disposant que de quelques kilo-octets de RAM/ROM avec un CPU …
  datacite :none 2021 置信度 0.66 Informatique
• A hardware-aware neural architecture search algorithm for wearable robotics
  Hardware-aware neural architecture search (HW NAS), the process of automating the design of neural architectures taking into consideration hardware constraints, has already outperformed the best human designs on many tasks. However, it is known to be highly de…
  datacite Garavagno, Andrea Mattia, Ragusa, Edoardo, Gastaldo, Paolo, Frisoli, Antonio 2023 置信度 0.66 Wearable RoboticsHardware Aware Neural Architecture SearchTinyMLConvolutional Neural NetworksMicrocontrollers
• A hardware-aware neural architecture search algorithm for wearable robotics
  Hardware-aware neural architecture search (HW NAS), the process of automating the design of neural architectures taking into consideration hardware constraints, has already outperformed the best human designs on many tasks. However, it is known to be highly de…
  datacite Garavagno, Andrea Mattia, Ragusa, Edoardo, Gastaldo, Paolo, Frisoli, Antonio 2023 置信度 0.66 Wearable RoboticsHardware Aware Neural Architecture SearchTinyMLConvolutional Neural NetworksMicrocontrollers
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