1. bookVolumen 22 (2022): Heft 3 (September 2022)
Zeitschriftendaten
License
Format
Zeitschrift
eISSN
1314-4081
Erstveröffentlichung
13 Mar 2012
Erscheinungsweise
4 Hefte pro Jahr
Sprachen
Englisch
Uneingeschränkter Zugang

Hardware Response and Performance Analysis of Multicore Computing Systems for Deep Learning Algorithms

Online veröffentlicht: 22 Sep 2022
Volumen & Heft: Volumen 22 (2022) - Heft 3 (September 2022)
Seitenbereich: 68 - 81
Eingereicht: 10 Jan 2022
Akzeptiert: 08 Jun 2022
Zeitschriftendaten
License
Format
Zeitschrift
eISSN
1314-4081
Erstveröffentlichung
13 Mar 2012
Erscheinungsweise
4 Hefte pro Jahr
Sprachen
Englisch

1. Zu, Y. Deep Learning Parallel Computing and Evaluation for Embedded System Clustering Architecture Processor. – Design Automation Embedded System, Vol. 24, 2020, pp. 145-159.10.1007/s10617-020-09235-5 Search in Google Scholar

2. Gomatheeshwari, B., J. Selvakumar. Appropriate Allocation of Workloads on Performance Asymmetric Multicore Architectures via Deep Learning Algorithms. – Microprocessors and Microsystems, Vol. 73, 2020, 102996. ISSN 0141-9331.10.1016/j.micpro.2020.102996 Search in Google Scholar

3. Mittal, S. A Survey on Optimized Implementation of Deep Learning Models on the NVIDIA Jetson Platform. – Journal of Systems Architecture, Vol. 97, 2019, pp. 428-442. ISSN 1383-7621.10.1016/j.sysarc.2019.01.011 Search in Google Scholar

4. Demir, B., S. Ertürk. Improving SVM Classification Accuracy Using a Hierarchical Approach for Hyperspectral Images. – In: Proc. of 16th IEEE International Conference on Image Processing (ICIP’09). IEEE, 2009.10.1109/ICIP.2009.5414491 Search in Google Scholar

5. Sultana, F., A. Sufian, P. Dutta. Advancements in Image Classification Using Convolutional Neural Network. – In: Proc. of 4th International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN’18), IEEE, 2018.10.1109/ICRCICN.2018.8718718 Search in Google Scholar

6. Salimi, B. S., B. Dewantara, I. K. Wibowo. Visual-Based Trash Detection and Classification System for Smart Trash Bin Robot. – In: Proc. of International Electronics Symposium on Knowledge Creation and Intelligent Computing (IES-KCIC’18), Bali, Indonesia, 2018, pp. 378-383. DOI: 10.1109/KCIC.2018.8628499. DOI öffnenSearch in Google Scholar

7. Ramcharan, A., et al. Deep Learning for Image-Based Cassava Disease Detection. – Frontiers in Plant Science, Vol. 8, 2017, p. 1852.10.3389/fpls.2017.01852 Search in Google Scholar

8. Basulto-Lantsova, J., A. Padilla-Medina, F. J. Perez-Pinal, A. I. Barranco-Gutierrez. Performance Comparative of OpenCV Template Matching Method on Jetson TX2 and Jetson Nano Developer Kits. – In: Proc. of 10th Annual Computing and Communication Workshop and Conference (CCWC’20), 2020, pp. 0812-0816.10.1109/CCWC47524.2020.9031166 Search in Google Scholar

9. Kim, S., S. Song, J. Kim, Z. Yuan, J. Cho. Fast Rotation-Invariant Template Matching with Candidate Reduction Using CUDA. – In: Proc. of International Symposium on Consumer Electronics (ISCE’15), 2015, pp. 1-2.10.1109/ISCE.2015.7177792 Search in Google Scholar

10. Hangün, B., Ö. Eyecioğlu. Performance Comparison between OpenCV Built in CPU and GPU Functions on Image Processing Operations. – International Journal of Engineering Science and Application, Vol. 1, 2017, pp. 34-41. Search in Google Scholar

11. Tair A. salih, Mohammad Basman Gh. A Novel Face Recognition System Based on Jetson Nano Developer Kit. – IOP Conference Series: Materials Science and Engineering, Vol. 928, 2020, No 3, IOP Publishing.10.1088/1757-899X/928/3/032051 Search in Google Scholar

12. Kumar, L., D. K. Singh. Analyzing Computational Response and Performance of Deep Convolution Neural Network for Plant Disease Classification Using Plant Leave Dataset. – In: Proc. of 10th IEEE International Conference on Communication Systems and Network Technologies (CSNT’21), 2021, pp. 549-553. DOI: 10.1109/CSNT51715.2021.9509632. DOI öffnenSearch in Google Scholar

13. Singh, Dushyant Kumar. 3D-CNN Based Dynamic Gesture Recognition for Indian Sign Language Modeling. – Procedia Computer Science, Vol. 189, 2021, pp. 76-83.10.1016/j.procs.2021.05.071 Search in Google Scholar

14. Shahid, A., M. Mushtaq. A Survey Comparing Specialized Hardware and Evolution in TPUs For Neural Networks. – In: Proc. of 23rd International Multitopic Conference (INMIC’20). IEEE, 2020.10.1109/INMIC50486.2020.9318136 Search in Google Scholar

15. Gao, F., Z. Huang, S. Wang et al. Optimized Parallel Implementation of Face Detection Based on Embedded Heterogeneous Many-Core Architecture. – Int. J. Pattern Recognit. Artif. Intell., Vol. 31, 2017, No 7, 1756011.10.1142/S0218001417560110 Search in Google Scholar

16. Yin, S., O. Peng, S. Tang et al. A High Energy Efficient Reconfigurable Hybrid Neural Network Processor for Deep Learning Applications. – IEEE J. Solid State Circuits, Vol. 53, 2018, No 4, pp. 968-982.10.1109/JSSC.2017.2778281 Search in Google Scholar

17. Wen, S., H. Wei, Z. Zeng et al. Memristive Fully Convolutional Network: An Accurate Hardware Image-Segmentor in Deep Learning. – IEEE Trans. Emerg. Top Comput. Intell., Vol. 2, 2018, No 5, pp. 324-334.10.1109/TETCI.2018.2829911 Search in Google Scholar

18. Sugie, T., T. Akamatsu, T. Nishitsuji et al. High-Performance Parallel Computing for Next-Generation Holographic Imaging. – Nat Electron, Vol. 1, 2018, No 4, pp. 254-259.10.1038/s41928-018-0057-5 Search in Google Scholar

19. Thoman, P., K. Dichev, T. Heller et al. A Taxonomy of Task-Based Parallel Programming Technologies for High-Performance Computing. – J. Supercomput., Vol. 74, 2018, No 4, pp. 1422-1434.10.1007/s11227-018-2238-4 Search in Google Scholar

20. Ansari, M. A., D. K. Singh. Review of Deep Learning Techniques for Object Detection and Classification. – In: Proc. of International Conference on Communication, Networks and Computing. Springer, Singapore, 2018.10.1007/978-981-13-2372-0_37 Search in Google Scholar

21. Hayashi, N., et al. Advanced Embedded Packaging for Power Devices. – In: Proc. of IEEE 67th Electronic Components and Technology Conference (ECTC’17), 2017, pp. 696-703. DOI: 10.1109/ECTC.2017.215. DOI öffnenSearch in Google Scholar

22. Lechner, M., A. Jantsch. Blackthorn: Latency Estimation Framework for CNNs on Embedded Nvidia Platforms. – IEEE Access, Vol. 9, 2021, pp. 110074-110084. DOI: 10.1109/ACCESS.2021.3101936. DOI öffnenSearch in Google Scholar

23. Vreča, J., et al. Accelerating Deep Learning Inference in Constrained Embedded Devices Using Hardware Loops and a Dot Product Unit. – IEEE Access, Vol. 8, 2020, pp. 165913-165926. DOI: 10.1109/ACCESS.2020.3022824. DOI öffnenSearch in Google Scholar

24. Tychalas, D., A. Keliris, M. Maniatakos. Stealthy Information Leakage through Peripheral Exploitation in Modern Embedded Systems. – IEEE Transactions on Device and Materials Reliability, Vol. 20, June 2020, No 2, pp. 308-318. DOI: 10.1109/TDMR.2020.2994016. DOI öffnenSearch in Google Scholar

25. Neelam, D., D. K. Singh. Review of Deep Learning Techniques for Gender Classification in Images. – In: Proc. of Harmony Search and Nature Inspired Optimization Algorithms. Springer, Singapore, 2019. 1089-1099.10.1007/978-981-13-0761-4_102 Search in Google Scholar

26. Luckow, M. C., N. Ashcraft, E. Weill, E. Djerekarov, B. Vorster. Deep Learning in the Automotive Industry: Applications and Tools. – In: Proc. of IEEE Int. Conf. Big Data, December 2016, pp. 3759-3768.10.1109/BigData.2016.7841045 Search in Google Scholar

27. Devyatkin, V., D. M. Filatov. Neural Network Traffic Signs Detection System Development. – In: Proc. of XXII Int. Conf. Soft Comput. Meas. (SCM’19)), May 2019, pp. 125-128.10.1109/SCM.2019.8903787 Search in Google Scholar

Empfohlene Artikel von Trend MD

Planen Sie Ihre Fernkonferenz mit Scienceendo