NDLI: A Flexible Scalable Hardware Architecture for Radial Basis Function Neural Networks

Content Provider	IEEE Xplore Digital Library
Author	Mohammadi, Mahnaz Satpute, Nitin Ronge, Rohit Chandiramani, Jayesh Nandy, S.K. Raihan, Aamir Verma, Tanmay Narayan, Ranjani Bhattacharya, Sukumar
Copyright Year	2015
Abstract	Radial Basis Function Neural Networks (RBFNN) are used in variety of applications such as pattern recognition, control and time series prediction and nonlinear identification. RBFNN with Gaussian Function as the basis function is considered for classification purpose. Training is done offline using K-means clustering method for center learning and Pseudo inverse for weight adjustments. Offline training is done since the objective function with any fixed set of weights can be computed and we can see whether we make any progress in training. Moreover, minimum of the objective function can be computed to any desired precision, while with online training none of these can be done and it is more difficult and unreliable. In this paper we provide the comparison of RBFNN implementation on FPGAs using soft core processor based multi-processor system versus a network of Hyper Cells [8], [13]. Next we propose three different partitioning structures (Linear, Tree and Hybrid) for the implementation of RBFNN of large dimensions. Our results show that implementation of RBFNN on a network of Hyper Cells using Hybrid Structure, has on average 26x clock cycle reduction and 105X improvement in the performance over that of multi-processor system on FPGAs.
Starting Page	505
Ending Page	510
File Size	467816
Page Count	6
File Format	PDF
ISBN	9781479966585
ISSN	10639667
DOI	10.1109/VLSID.2015.91
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-01-03
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Reconfigurable Architecture Multi Processor System on Chip Pattern Recognition Radial Basis Function Neural Network
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering Hardware and Architecture

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