Memristor based object detection using neural network

被引：5
作者：
Ravikumar, Ki [1 ,2 ]
Sukumar, R. [1 ]
机构：
[1] Jain Univ, Dept E&CE, Bangalore, Karnataka, India
[2] Jain Inst Technol, Dept E&CE, Davangere, Karnataka, India
来源：
HIGH-CONFIDENCE COMPUTING | 2022年 / 2卷 / 04期
关键词：
Memristor; Deep learning; Object detection; CIFAR-10;
D O I：
10.1016/j.hcc.2022.100085
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
With the increasing growth of AI, big data analytics, cloud computing, and Internet of Things applications, developing memristor devices and related hardware systems to compute the deep learning application needs extensive data calculations with low power consumption and lesser chip area. Deep learning model is one of the AI methods which is gaining importance in object detection, natural language processing, and pattern recognition. A large amount of data handling is essential for driving the deep learning model with less power consumption. To address these issues, the paper proposed the Memristor-based object detection on the CIFAR-10 dataset and achieved an accuracy of 85 percent. The memtorch package in python is employed to predict the objects for implementation.
引用
收藏
页数：5
相关论文
共 50 条
  • [41] Memristor Crossbar Deep Network Implementation Based on a Convolutional Neural Network
    Yakopcic, Chris
    Alom, Md Zahangir
    Taha, Tarek M.
    2016 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), 2016, : 963 - 970
  • [42] AN IMPROVED OBJECT DETECTION METHOD BASED ON DEEP CONVOLUTION NEURAL NETWORK FOR SMOKE DETECTION
    Zeng, Junying
    Lin, Zuoyong
    Qi, Chuanbo
    Zhao, Xiaoxiao
    Wang, Fan
    PROCEEDINGS OF 2018 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS (ICMLC), VOL 1, 2018, : 184 - 189
  • [43] Convolutional neural network: a review of models, methodologies and applications to object detection
    Dhillon, Anamika
    Verma, Gyanendra K.
    PROGRESS IN ARTIFICIAL INTELLIGENCE, 2020, 9 (02) : 85 - 112
  • [44] Convolutional neural network: a review of models, methodologies and applications to object detection
    Anamika Dhillon
    Gyanendra K. Verma
    Progress in Artificial Intelligence, 2020, 9 : 85 - 112
  • [45] Airspace Object Detection Above the Guarded Area Using Segmentation Neural Network
    Stursa, Dominik
    Dolezel, Petr
    Merta, Jan
    PROCEEDINGS OF THE FUTURE TECHNOLOGIES CONFERENCE (FTC) 2021, VOL 2, 2022, 359 : 283 - 292
  • [46] Parking lot delineation and object detection using a localized Convolutional Neural Network
    Cisek, Daniel
    Dale, Jedidiah
    Pepper, Susan
    Mahajan, Manoj
    Yoo, Shinjae
    2016 NEW YORK SCIENTIFIC DATA SUMMIT (NYSDS), 2016,
  • [47] Deep Neural Network Pruning Based Two-Stage Remote Sensing Image Object Detection
    Wang S.-S.
    Wang M.
    Wang G.-Y.
    Dongbei Daxue Xuebao/Journal of Northeastern University, 2019, 40 (02): : 174 - 179
  • [48] Keypoint Density-based Region Proposal for Fine-Grained Object Detection using Regions with Convolutional Neural Network Features
    Turner, J. T.
    Gupta, Kalyan Moy
    Aha, David
    2016 IEEE APPLIED IMAGERY PATTERN RECOGNITION WORKSHOP (AIPR), 2016,
  • [49] Convolutional neural network based object detection system for video surveillance application
    Bhimavarapu, John Philip
    Ramaraju, Sriharsha
    Nagajyothi, Dimmita
    Rao, Inumula Veeraraghava
    CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE, 2023, 35 (03)
  • [50] Memristor Based Chaotic Neural Network with Application in Nonlinear Cryptosystem
    Prasad, N. Varsha
    Tumu, Sriharini
    Bevi, A. Ruhan
    ENGINEERING APPLICATIONS OF NEURAL NETWORKS, EANN 2017, 2017, 744 : 49 - 60
12345