Northwestern University

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Scalable Parallelization Strategy for Large-Scale Deep Learning

Description:

Recently, a myriad of applications take advantage of deep learning methods to solve regression/classification problems. Although deep neural networks have shown powerful learning capability, many deep learning applications suffer from the extremely time-consuming training of the neural networks. In order to reduce the training time, researchers usually consider parallel training...

Keyword:

Adaptive Batch Size, Data Parallelism, Deep Learning, and Communication Cost

Subject:

Computer engineering

Creator:

Lee, Sunwoo N/A

Owner:

Scholarly Digital Publishing

Language:

en

Date Uploaded:

02/01/2021

Date Created:

2020-01-01

Resource Type:

Dissertation

Alternate Identifier:

etdadmin_upload_772590 and http://dissertations.umi.com/northwestern:15340