Hyperspectral Image Classification Using Parallel Autoencoding Diabolo Networks on Multi-Core and Many-Core Architectures

被引:5
|
作者
Torti, Emanuele [1 ]
Fontanella, Alessandro [1 ]
Plaza, Antonio [2 ]
Plaza, Javier [2 ]
Leporati, Francesco [1 ]
机构
[1] Univ Pavia, Dept Elect Comp & Biomed Engn, I-27100 Pavia, Italy
[2] Univ Extremadura, Dept Technol Comp & Commun, ES-10071 Caceres, Spain
来源
ELECTRONICS | 2018年 / 7卷 / 12期
关键词
Graphics Processing Units (GPUs); multi-core CPU; parallel processing; CUDA; OpenMP; hyperspectral imaging; IMPLEMENTATION;
D O I
10.3390/electronics7120411
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
One of the most important tasks in hyperspectral imaging is the classification of the pixels in the scene in order to produce thematic maps. This problem can be typically solved through machine learning techniques. In particular, deep learning algorithms have emerged in recent years as a suitable methodology to classify hyperspectral data. Moreover, the high dimensionality of hyperspectral data, together with the increasing availability of unlabeled samples, makes deep learning an appealing approach to process and interpret those data. However, the limited number of labeled samples often complicates the exploitation of supervised techniques. Indeed, in order to guarantee a suitable precision, a large number of labeled samples is normally required. This hurdle can be overcome by resorting to unsupervised classification algorithms. In particular, autoencoders can be used to analyze a hyperspectral image using only unlabeled data. However, the high data dimensionality leads to prohibitive training times. In this regard, it is important to realize that the operations involved in autoencoders training are intrinsically parallel. Therefore, in this paper we present an approach that exploits multi-core and many-core devices in order to achieve efficient autoencoders training in hyperspectral imaging applications. Specifically, in this paper, we present new OpenMP and CUDA frameworks for autoencoder training. The obtained results show that the CUDA framework provides a speed-up of about two orders of magnitudes as compared to an optimized serial processing chain.
引用
收藏
页数:15
相关论文
共 50 条
  • [1] Parallel Subspace Clustering Using Multi-core and Many-core Architectures
    Datta, Amitava
    Kaur, Amardeep
    Lauer, Tobias
    Chabbouh, Sami
    [J]. NEW TRENDS IN DATABASES AND INFORMATION SYSTEMS, ADBIS 2017, 2017, 767 : 213 - 223
  • [2] Parallel Implementations of the Cooperative Particle Swarm Optimization on Many-core and Multi-core Architectures
    Nadia Nedjah
    Rogério de M. Calazan
    Luiza de Macedo Mourelle
    Chao Wang
    [J]. International Journal of Parallel Programming, 2016, 44 : 1173 - 1199
  • [3] Fast parallel beam propagation method based on multi-core and many-core architectures
    Shaaban, Adel
    Sayed, M.
    Hameed, Mohamed Farhat O.
    Saleh, Hassan, I
    Gomaa, L. R.
    Du, Yi-Chun
    Obayya, S. S. A.
    [J]. OPTIK, 2019, 180 : 484 - 491
  • [4] Parallel Implementations of the Cooperative Particle Swarm Optimization on Many-core and Multi-core Architectures
    Nedjah, Nadia
    Calazan, Rogerio de M.
    Mourelle, Luiza de Macedo
    Wang, Chao
    [J]. INTERNATIONAL JOURNAL OF PARALLEL PROGRAMMING, 2016, 44 (06) : 1173 - 1199
  • [5] Revision of Relational Joins for Multi-Core and Many-Core Architectures
    Krulis, Martin
    Yaghob, Jakub
    [J]. DATESO 2011: DATABASES, TEXTS, SPECIFICATIONS, OBJECTS, 2011, 706 : 229 - 240
  • [6] Solving Matrix Equations on Multi-Core and Many-Core Architectures
    Benner, Peter
    Ezzatti, Pablo
    Mena, Hermann
    Quintana-Orti, Enrique S.
    Remon, Alfredo
    [J]. ALGORITHMS, 2013, 6 (04) : 857 - 870
  • [7] RTL Test Generation on Multi-Core and Many-Core Architectures
    Varadarajan, Aravind Krishnan
    Hsiao, Michael S.
    [J]. 2019 32ND INTERNATIONAL CONFERENCE ON VLSI DESIGN AND 2019 18TH INTERNATIONAL CONFERENCE ON EMBEDDED SYSTEMS (VLSID), 2019, : 100 - 105
  • [8] PARALLEL SPN ON MULTI-CORE CPUS AND MANY-CORE GPUS
    Kirschenmann, W.
    Plagne, L.
    Poncot, A.
    Vialle, S.
    [J]. TRANSPORT THEORY AND STATISTICAL PHYSICS, 2010, 39 (2-4): : 255 - 281
  • [9] SPECTR: Scalable Parallel Short Read Error Correction on Multi-core and Many-core Architectures
    Xu, Kai
    Kobus, Robin
    Chan, Yuandong
    Gao, Ping
    Meng, Xiangxu
    Wei, Yanjie
    Schmidt, Bertil
    Liu, Weiguo
    [J]. PROCEEDINGS OF THE 47TH INTERNATIONAL CONFERENCE ON PARALLEL PROCESSING, 2018,
  • [10] A Fine-Grained Parallel Particle Swarm Optimization on Many-core and Multi-core Architectures
    Nedjah, Nadia
    Calazan, Rogerio de Moraes
    Mourelle, Luiza de Macedo
    [J]. PARALLEL COMPUTING TECHNOLOGIES (PACT 2017), 2017, 10421 : 215 - 224