Embedding Convolutional Neural Network (CNN) into edge devices for inference is a very challenging task because such lightweight hardware is not born to handle this heavyweight software, which is the common overhead from the modern state-of-the-art CNN models. In this paper, targeting at reducing the overhead with trading the accuracy as less as possible, we propose a novel of Network Candidate Search (NCS), an alternative way to study the trade-off between the resource usage and the performance through grouping concepts and elimination tournament. Besides, NCS can also be generalized across any neural network. In our experiment, we collect candidate CNN models from EfficientNet-B0 to be scaled down in varied way through width, depth, input resolution and compound scaling down, applying NCS to research the scaling-down trade-off. Meanwhile, a family of extremely lightweight EfficientNet is obtained, called EfficientNet-eLite. For further embracing the CNN edge application with Application-Specific Integrated Circuit (ASIC), we adjust the architectures of EfficientNet-eLite to build the more hardware-friendly version, EfficientNet-HF. Evaluation on ImageNet dataset, both proposed EfficientNet-eLite and EfficientNet-HF present better parameter usage and accuracy than the previous start-of-the-art CNNs. Particularly, the smallest member of EfficientNet-eLite is more lightweight than the best and smallest existing MnasNet with 1.46x less parameters and 0.56% higher accuracy. Code is available at https://github.com/Ching-Chen-Wang/EfficientNet-eLite
Automated Plant Disease and Pest Detection System Using Hybrid Lightweight CNN-MobileViT Models for Diagnosis of Indigenous Crops
Department of Computer Science \& Engineering；Department of Information Technology Mekelle University - Mekelle Institute of Technology, Ethiopia Email
Lightweight Image Classification of Raptor Species for Edge Devices: Rare-Species Dataset Expansion via Video Frame Extraction, Knowledge Distillation, and TensorRT Deployment
Swin Meets EfficientNet: Lightweight Architectures for GAN-Based Face Forensics
International Institute of Information Technology, Bangalore（国际信息技术学院（班加罗尔））；Ulster University（阿尔斯特大学）；Dr B R Ambedkar National Institute of Technology Jalandhar（阿姆倍伽尔博士国家技术学院贾朗达尔分校）
XMACNet: An Explainable Lightweight Attention based CNN with Multi Modal Fusion for Chili Disease Classification
Vellore Institute of Technology（韦洛尔理工学院）；Angel College of Engineering and Technology（安吉尔工程技术学院）；Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology（Vel Tech朗加拉詹·萨贡塔拉博士科技研究院）；Bannari Amman Institute of Technology（班纳里·安曼理工学院）
SugarcaneShuffleNet: A Very Fast, Lightweight Convolutional Neural Network for Diagnosis of 15 Sugarcane Leaf Diseases
Department of Robotics and Mechatronics Engineering, University of Dhaka, Dhaka 1000, Bangladesh（机器人与机电工程系，达卡大学，达卡1000，孟加拉国）；GIS and Remote Sensing Lab, Gazipur Agricultural University, Gazipur 1706, Bangladesh（地理信息系统与遥感实验室，加兹ipur农业大学，加兹ipur 1706，孟加拉国）；Bangladesh Sugarcrop Research Institute, Ishwardi, Pabna, Bangladesh（孟加拉国甘蔗作物研究 institute，Ishwardi，Pabna，孟加拉国）；Department of Computer Science and Engineering, University of Dhaka, Dhaka 1000, Bangladesh（计算机科学与工程系，达卡大学，达卡1000，孟加拉国）
Do Newer Lightweight CNNs Perform Better Under Resource Constraints? A Controlled Multigenerational Study of Architecture, Initialization, Training Budget, and Efficiency
Efficient Feature Extraction Using Light-Weight CNN Attention-Based Deep Learning Architectures for Ultrasound Fetal Plane Classification
Amrita School of Artificial Intelligence（阿米特学校人工智能学院）；Amrita Vishwa Vidyapeetham（阿米特世界大学）；Center for Artificial Intelligence（人工智能中心）；Prince Mohammed Bin Fahd University（普里姆·穆罕默德·本·法赫德大学）
Stain Normalized Breast Histopathology Image Recognition using Convolutional Neural Networks for Cancer Detection
Center for Wireless Networks & Applications (WNA), Amrita Vishwa Vidyapeetham（阿姆里塔·维什瓦·维迪亚皮塔姆大学无线网络与应用中心）；Valeo（法雷奥）；Department of Computer Science and Engineering, Amrita Vishwa Vidyapeetham（阿姆里塔·维什瓦·维迪亚皮塔姆大学计算机科学与工程系）；School of Computing and Electrical Engineering, IIT Mandi（印度理工学院曼迪分校计算与电气工程学院）
Exploring convolutional neural networks with transfer learning for diagnosing Lyme disease from skin lesion images
Université Clermont Auvergne（克莱蒙奥弗涅大学）；CNRS（法国国家科学研究中心）；ENSMSE（圣艾蒂安国立矿业工程师学校）；INRAE（法国国家农业食品与环境研究院）；VetAgro Sup（法国兽医与农学高等学院）；Université de Lyon（里昂大学）；CHU Clermont-Ferrand（克莱蒙费朗大学医院）；Inserm（法国国家健康与医学研究院）；Tous Chercheurs Laboratory（全民研究者实验室）；CPIE Champenoux（尚佩努环境倡议中心）；University Hospital of Nancy（南锡大学医院）；University Hospital of Saint Etienne（圣艾蒂安大学医院）；IHU-Méditerranée Infection（地中海感染大学医院研究所）；Aix Marseille Univ（艾克斯-马赛大学）；IRD（法国发展研究院）；AP-HM（马赛公立医院援助机构）；SSA（法国军队卫生勤务局）；Hôpitaux Universitaires（斯特拉斯堡大学医院）；Pontchaillou University Hospital（蓬查卢大学医院）；CHR Orléans（奥尔良地区中心医院）；Hospital of Villeneuve-Saint-Georges（维勒讷夫圣乔治医院）；Centre Hospitalier Universitaire de Rennes（雷恩大学医院）
Balancing Accuracy and Efficiency: CNN Fusion Models for Diabetic Retinopathy Screening
Department of Electrical and Computer Engineering（电气与计算机工程系）；North South University（北南大学）；Digital Health Research Division（数字健康研究部）；Korea Institute of Oriental Medicine（韩国东方医学研究院）；Department of Computer Science（计算机科学系）；American International University--Bangladesh（美国国际大学-孟加拉国）
Analysis of Hyperparameter Optimization Effects on Lightweight Deep Models for Real-Time Image Classification
Homi Bhabha National Institute（霍米·巴巴国家研究所）；Variable Energy Cyclotron Centre（可变能量回旋加速器中心）；Gargi Memorial Institute of Technology（加尔吉纪念技术学院）；Jadavpur University（贾达普大学）
Squeezed-Eff-Net: Edge-Computed Boost of Tomography Based Brain Tumor Classification leveraging Hybrid Neural Network Architecture
Department of Computer Science and Engineering, International University of Business Agriculture and Technology (IUBAT)（计算机科学与工程系，国际大学商学院农业与技术（IUBAT））；Department of Computer Science and Engineering, United International University (UIU)（计算机科学与工程系，联合国际大学（UIU））
GroupNL: Low-Resource and Robust CNN Design over Cloud and Device
School of Artificial Intelligence, Beijing Normal University（人工智能学院，北京师范大学）；School of Computer Science and Technology, Beijing Jiaotong University（计算机科学与技术学院，北京交通大学）；Department of Computer Science, City University of Hong Kong（计算机科学系，城市大学）；School of Computer and Information Engineering, Henan Normal University（计算机与信息工程学院，河南师范大学）；Department of Computer Science, National Textile University Faisalabad（计算机科学系，国立纺织大学费萨尔巴德分校）；State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications（网络与交换技术国家重点实验室，北京邮电大学）；Department of Computing, The Hong Kong Polytechnic University（计算系，香港理工大学）
