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Proceedings Paper

Optimized hardware framework of MLP with random hidden layers for classification applications
Author(s): Abdullah M. Zyarah; Abhishek Ramesh; Cory Merkel; Dhireesha Kudithipudi
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Paper Abstract

Multilayer Perceptron Networks with random hidden layers are very efficient at automatic feature extraction and offer significant performance improvements in the training process. They essentially employ large collection of fixed, random features, and are expedient for form-factor constrained embedded platforms. In this work, a reconfigurable and scalable architecture is proposed for the MLPs with random hidden layers with a customized building block based on CORDIC algorithm. The proposed architecture also exploits fixed point operations for area efficiency. The design is validated for classification on two different datasets. An accuracy of ~ 90% for MNIST dataset and 75% for gender classification on LFW dataset was observed. The hardware has 299 speed-up over the corresponding software realization.

Paper Details

Date Published: 12 May 2016
PDF: 11 pages
Proc. SPIE 9850, Machine Intelligence and Bio-inspired Computation: Theory and Applications X, 985007 (12 May 2016); doi: 10.1117/12.2225498
Show Author Affiliations
Abdullah M. Zyarah, Rochester Institute of Technology (United States)
Univ. of Baghdad (Iraq)
Abhishek Ramesh, Rochester Institute of Technology (United States)
Cory Merkel, Rochester Institute of Technology (United States)
Dhireesha Kudithipudi, Rochester Institute of Technology (United States)


Published in SPIE Proceedings Vol. 9850:
Machine Intelligence and Bio-inspired Computation: Theory and Applications X
Misty Blowers; Jonathan Williams; Russell D. Hall, Editor(s)

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