Otwarty dostęp

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


Zacytuj

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. Open DOISearch 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. Open DOISearch 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. Open DOISearch 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. Open DOISearch 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. Open DOISearch 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. Open DOISearch 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

eISSN:
1314-4081
Język:
Angielski
Częstotliwość wydawania:
4 razy w roku
Dziedziny czasopisma:
Computer Sciences, Information Technology