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FPGA Implementation & Acceleration o...

Deshpande, Mandar.

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FPGA Implementation & Acceleration of Building blocks for Biologically Inspired Computational Models.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	FPGA Implementation & Acceleration of Building blocks for Biologically Inspired Computational Models./
Author:	Deshpande, Mandar.
Description:	100 p.
Notes:	Source: Masters Abstracts International, Volume: 49-05, page: 3289.
Contained By:	Masters Abstracts International49-05.
Subject:	Engineering, Electronics and Electrical. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1491185
ISBN:	9781124593487

FPGA Implementation & Acceleration of Building blocks for Biologically Inspired Computational Models.
Deshpande, Mandar.

FPGA Implementation & Acceleration of Building blocks for Biologically Inspired Computational Models. - 100 p.

Source: Masters Abstracts International, Volume: 49-05, page: 3289.

Thesis (M.S.)--Portland State University, 2011.

In recent years there has been significant research in the field of computational neuroscience and many of these biologically inspired cognitive models are based on the theory of operation of mammalian visual cortex. One such model of neocortex developed by George & Hawkins, known as Hierarchical Temporal Memories (HTM), is considered for the research discussed here. We propose a simple hierarchical model that is derived from HTM. The aim of this work is to evaluate the hardware cost and performance against software based simulations.

ISBN: 9781124593487Subjects--Topical Terms:

626636
Engineering, Electronics and Electrical.

FPGA Implementation & Acceleration of Building blocks for Biologically Inspired Computational Models.
LDR:02265nam 2200325 4500 001 1403108
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020 $a 9781124593487
035 $a (UMI)AAI1491185
035 $a AAI1491185
040 $a UMI $c UMI
100 1 $a Deshpande, Mandar. $3 1682353
245 1 0 $a FPGA Implementation & Acceleration of Building blocks for Biologically Inspired Computational Models.
300 $a 100 p.
500 $a Source: Masters Abstracts International, Volume: 49-05, page: 3289.
500 $a Adviser: Dan Hammerstrom.
502 $a Thesis (M.S.)--Portland State University, 2011.
520 $a In recent years there has been significant research in the field of computational neuroscience and many of these biologically inspired cognitive models are based on the theory of operation of mammalian visual cortex. One such model of neocortex developed by George & Hawkins, known as Hierarchical Temporal Memories (HTM), is considered for the research discussed here. We propose a simple hierarchical model that is derived from HTM. The aim of this work is to evaluate the hardware cost and performance against software based simulations.
520 $a This work presents a detailed hardware implementation and analysis of the derived hierarchical model. We show that these networks are inherently parallel in their architecture, similar to the biological computing, and that parallelism can be exploited by massively parallel architectures implemented using reconfigurable devices such as the FPGA. Hardware implementation accelerates the learning process which is useful in many real world problems. We have implemented a complex network node that operates in real time using an FPGA. The current architecture is modular and allows us to estimate the hardware resources and computational units required to realize large scale networks in the future.
590 $a School code: 0180.
650 4 $a Engineering, Electronics and Electrical. $3 626636
650 4 $a Biology, Bioinformatics. $3 1018415
650 4 $a Computer Science. $3 626642
690 $a 0544
690 $a 0715
690 $a 0984
710 2 $a Portland State University. $b Electrical and Computer Engineering. $3 1678929
773 0 $t Masters Abstracts International $g 49-05.
790 1 0 $a Hammerstrom, Dan, $e advisor
790 1 0 $a Song, Xiaoyu $e committee member
790 1 0 $a Hall, Douglas V. $e committee member
790 $a 0180
791 $a M.S.
792 $a 2011
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1491185