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A Very Compact Embedded CNN Processor Design Based on Logarithmic Computing
[article]
2020
arXiv
pre-print
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The proposed approach has been extensively evaluated on many popular image classification CNN models (AlexNet, VGG16, and ResNet-18/34) and object detection models (Yolov2). ...
In this paper, we propose a very compact embedded CNN processor design based on a modified logarithmic computing method using very low bit-width representation. ...
Compared to other object detection networks, it uses a single neural network to predict object bounding boxes and class probabilities in a single assessment, so it is more suitable for execution in embedded ...
arXiv:2010.11686v1
fatcat:2iwfsacfn5befkqr6j2pimohre
Approximation Computing Techniques to Accelerate CNN Based Image Processing Applications – A Survey in Hardware/Software Perspective
2020
International Journal of Advanced Trends in Computer Science and Engineering
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In today's technology era, Convolutional Neural Networks (CNNs) are the limelight for various cognitive tasks because of their high accuracy. ...
The survey has been conducted by considering different metrics: approximation technique used, datasets used for evaluation, network structure (AlexNet, LeNet, Visual Geometry Group (VGG) ), hardware platform ...
If the weights and activations of the CNNs are described in the binary format, then those neural networks are called Binarized Neural Networks (BNNs). There are two types of binarization: 1. ...
doi:10.30534/ijatcse/2020/202932020
fatcat:k3qozwldifeedp5sx5x2o37oyu
Performance Evaluation of Convolutional Networks on Heterogeneous Architectures for Applications in Autonomous Robotics
2022
Tecno Lógicas
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Xilinx® Ultra96™, Intel® Cyclone® V-SoC and NVIDIA® Jetson™ TX2 cards were used, and Tinier-YOLO, AlexNet, Inception-V1 and Inception V3 transfer-learning networks were executed. ...
In each embedded system, an object recognition stage is performed using commercial convolutional neural network acceleration frameworks. ...
, validation, investigation, review and editing, funding acquisition. ...
doi:10.22430/22565337.2170
doaj:9985655d602e4d67bd3cd10d7c0399c5
fatcat:ecdu7assqvea5guaxbvvkirsyq
Detecting Extratropical Cyclones of the Northern Hemisphere with Single Shot Detector
2022
Remote Sensing
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We trained and evaluated our model with our labeled dataset on two settings (binary and multiclass classifications), while keeping a record of the results. ...
We first labeled the cyclone center by adapting an approach from Bonfanti et al. in 2017 and set up criteria of labeling ETCs of three categories: developing, mature, and declining stages. ...
from deep convolutional neural networks, such as AlexNet and ResNet [4, 5] . ...
doi:10.3390/rs14020254
fatcat:bcx33o6c5fa4he4ezcfnxflocy
Towards Performing Image Classification and Object Detection with Convolutional Neural Networks in Autonomous Driving Systems: A Survey (December 2021)
2022
IEEE Access
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4 using ResNet [17] as the reference network to achieve a fair comparison. ...
INTRODUCTION In recent years, Deep Learning (DL) techniques have been exhaustively utilised in a large variety of fields, and Convolutional Neural Networks (CNNs) are one of the most frequently used of ...
His research focuses on both computer vision tasks for autonomous vehicles with deep learning techniques and optimization methods. ...
doi:10.1109/access.2022.3147495
fatcat:i4xtly3gizck3eorcqknbz4xdm
An optimized transfer learning-based approach for automatic diagnosis of COVID-19 from chest x-ray images
2021
PeerJ Computer Science
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using chest x-ray images. ...
The OTLD-COVID-19 approach adapts Manta-Ray Foraging Optimization (MRFO) algorithm to optimize the network hyperparameters' values of the CNN architectures to improve their classification performance. ...
the paper, and approved the final draft. ...
doi:10.7717/peerj-cs.555
pmid:34141886
pmcid:PMC8176553
fatcat:ekgfib5g5falzdt63ld7fejmqq
Investigations of Object Detection in Images/Videos Using Various Deep Learning Techniques and Embedded Platforms—A Comprehensive Review
2020
Applied Sciences
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Earlier traditional detection methods were used for detecting the objects with the introduction of convolutional neural networks. ...
This paper shows a detailed survey on recent advancements and achievements in object detection using various deep learning techniques. ...
In object classification application, manual feature extraction is eliminated by a convolutional neural network (CNN), so there is no need to manually identify features that are useful for image classification ...
doi:10.3390/app10093280
fatcat:e6jrltv6lrhxjntlhq7d34247e
HPC AI500: The Methodology, Tools, Roofline Performance Models, and Metrics for Benchmarking HPC AI Systems
[article]
2020
arXiv
pre-print
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We propose using convolution and GEMM -- the two most intensively-used kernel functions to measure the upper bound performance of the HPC AI systems, and present HPC AI roofline models for guiding performance ...
On the basis of AIBench -- by far the most comprehensive AI benchmarks suite, we present and build two HPC AI benchmarks from both business and scientific computing: Image Classification, and Extreme Weather ...
We also thank Shaomeng Cao, Xuhui Shao, Yongheng Liu, Changsong Liu, and Jingfei Qiu for technical support in using those systems. ...
arXiv:2007.00279v1
fatcat:mweupqwxffapxfid3kkdnvnroy
Comprehensive survey of deep learning in remote sensing: theories, tools, and challenges for the community
2017
Journal of Applied Remote Sensing
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In recent years, deep learning (DL), a re-branding of neural networks (NNs), has risen to the top in numerous areas, namely computer vision (CV), speech recognition, natural language processing, etc. ...
We also review recent new developments in the DL field that can be used in DL for RS. Namely, we focus on theories, tools and challenges for the RS community. ...
Acknowledgments The authors wish to thank graduate students Vivi Wei, Julie White, and Charlie Veal for their valuable inputs related to DL tools. ...
doi:10.1117/1.jrs.11.042609
fatcat:tdbssxma3fettcjy5iqgo6afwa
GeoAI for Large-Scale Image Analysis and Machine Vision: Recent Progress of Artificial Intelligence in Geography
2022
ISPRS International Journal of Geo-Information
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This paper provides a comprehensive overview of GeoAI research used in large-scale image analysis, and its methodological foundation, most recent progress in geospatial applications, and comparative advantages ...
in a variety of image analysis and machine vision tasks. ...
Acknowledgments: The authors sincerely appreciate Yingjie Hu and Song Gao for comments on an earlier version of the manuscript.
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/ijgi11070385
fatcat:yyzi46anyfcjrjuzcjfhbczo5y
Machine Learning in Disaster Management: Recent Developments in Methods and Applications
2022
Machine Learning and Knowledge Extraction
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Recent developments in artificial intelligence (AI) and especially in machine learning (ML) and deep learning (DL) have been used to better cope with the severe and often catastrophic impacts of disasters ...
assessment and post-disaster response as well as cases studies. ...
For the disaster-related binary classification, authors used DL and ML techniques. ...
doi:10.3390/make4020020
fatcat:wcdrh23k5ja6tdqlyhl7erobey
A Deep-Learning Approach to Soil Moisture Estimation with GNSS-R
2022
Remote Sensing
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With this network, a soil moisture product was generated using DDMs from 2017–2019 which is generally comparable to existing global soil moisture products, and shows potential advantages in spatial resolution ...
Comparisons with in-situ measurements demonstrate the correlation between the network predictions and ground truth with high temporal resolution. ...
Acknowledgments: The GEOS data used in this study/project were provided by the Global Modeling and Assimilation Office (GMAO) at NASA Goddard Space Flight Center. ...
doi:10.3390/rs14143299
fatcat:eur57lzvrrg2jjfktqegrarwoy
A Review of the Optimal Design of Neural Networks Based on FPGA
2022
Applied Sciences
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Deep learning based on neural networks has been widely used in image recognition, speech recognition, natural language processing, automatic driving, and other fields and has made breakthrough progress ...
In order to track the latest research results of neural network optimization technology based on FPGA in time and to keep abreast of current research hotspots and application fields, the related technologies ...
.; writing-review and editing, Z.L.; supervision, Z.L.; project administration, Z.L.; funding acquisition, Z.L. All authors have read and agreed to the published version of the manuscript. ...
doi:10.3390/app122110771
fatcat:k5qyqhc2qvgprcmvsz2rxq3hue
How can big data and machine learning benefit environment and water management: A survey of methods, applications, and future directions
2019
Environmental Research Letters
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The authors are grateful to Dr Michael Fienen and an anonymous reviewer for their constructive comments on the original manuscript. ...
[136] , and ResNet [137] . ...
Common file formats used in disseminating gridded data include ASCII, GRIB (gridded binary), netCDF (network common data form), and HDF (hierarchical data format). ...
doi:10.1088/1748-9326/ab1b7d
fatcat:vx4thuy45vhlnmhu7bk2hwh2g4
Detecting Extratropical Cyclones of the Northern Hemisphere with Single Shot Detector
[article]
2021
Preserved Fulltext
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We train and evaluate our model with our labeled dataset on two settings (binary and multiclass classifications), while keeping a record of the results. ...
In this paper, we propose a deep learning-based model to detect extratropical cyclones (ETCs) of northern hemisphere, while developing a novel workflow of processing images and generating labels for ETCs ...
adapted from deep convolutional neural networks such as AlexNet and ResNet [5, 6] . ...
doi:10.48550/arxiv.2112.01283
fatcat:elw64s74hzdjfj7aimjnjf3dau
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