Please use this identifier to cite or link to this item:
Title: A study of deep learning on multi-core processors
Authors: Ho Sy, Viet Anh
Keywords: DRNTU::Science
Issue Date: 2016
Abstract: The aim of this project is to conduct a study of deep learning on multi-core processors. The study is evaluated by benchmarking different deep learning frameworks on NVIDIA GPUs. The results of this project might serve as the guideline for choosing suitable deep learning frameworks to train deep learning models. Some popular deep learning frameworks and models are chosen to carry out the benchmark. For each deep learning model, the architecture is transferred into actual codes and configurations that utilize NVIDA GPUs to fasten the training process. The performance and hardware resources usage of each framework when running the models are measured and recorded in order to do the analysis and comparison later on. The results show that some frameworks outperform others in term of performance, while other frameworks demonstrate better GPU memory management. Therefore, based on the outcomes measured by this project, some frameworks should be preferred given a specific hardware details. However, this project does not benchmark the exhaustive list of all deep learning frameworks out there and should be extended to give future deep learning researchers broader view of the problem.
Rights: Nanyang Technological University
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Student Reports (FYP/IA/PA/PI)

Files in This Item:
File Description SizeFormat 
  Restricted Access
838.59 kBAdobe PDFView/Open

Google ScholarTM


Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.