Hardware based Analysis of Deep Neural Networks
| dc.contributor.author | Karia, A. | |
| dc.contributor.author | Patil, A. | |
| dc.contributor.author | Apoorva, M.K. | |
| dc.contributor.author | Varsha, P. | |
| dc.contributor.author | Pillay, S. | |
| dc.contributor.author | Mohan, B.R. | |
| dc.date.accessioned | 2026-02-06T06:35:06Z | |
| dc.date.issued | 2023 | |
| dc.description.abstract | The advent of the Machine Learning (ML) era is now evident and its inculcation into early education requires students to have feasible options to work with the field. The proposed comparative analysis tests different frameworks namely sci-kit with Keras, Tensor-flow and PyTorch on various available processors and discussed further using a range of standard metrics to evaluate the model and well as the underlying hardware that they will run upon. Based on the study, the most viable combination of framework and hardware for educational purpose shall be found out. © 2023 IEEE. | |
| dc.identifier.citation | IDCIoT 2023 - International Conference on Intelligent Data Communication Technologies and Internet of Things, Proceedings, 2023, Vol., , p. 971-975 | |
| dc.identifier.uri | https://doi.org/10.1109/IDCIoT56793.2023.10053400 | |
| dc.identifier.uri | https://idr.nitk.ac.in/handle/123456789/29637 | |
| dc.publisher | Institute of Electrical and Electronics Engineers Inc. | |
| dc.subject | Deep Learning | |
| dc.subject | Deep Neural Networks (DNN) | |
| dc.subject | Machine Learning | |
| dc.title | Hardware based Analysis of Deep Neural Networks |