Presenter(s)

Md. Zahangir Alom

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Description

Deep Learning (DL) has been showing huge success for analysis the big data problem. However, this large scale implementation of deep learning algorithms for Big Data analytics requires huge computing resources, leading to a high power requirement and communication overhead. Recently, IBM has developed a new non von Neumann architecture called TrueNorth Cognitive System which allows for a new direction of research of in the neuromorphic computing. We have implemented deep learning approach with different optimizer on the IBM’s TrueNorth system using Caffe, Tea and Corelet Programming Environment (CPE-2.1) which is experimented on MNIST dataset. The experimental results are analyzed for different optimization functions. In addition, we also implemented Intrusion detection for cyber security which being considered another big data problem. The experimental results show promising recognition accuracy for anomaly detection and classification.

Publication Date

4-5-2017

Project Designation

Graduate Research - Graduate

Primary Advisor

Tarek M. Taha

Primary Advisor's Department

Electrical and Computer Engineering

Keywords

Stander Symposium project

Deep Learning for Big Data Analytics in High-Performance Computing Environments

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