Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/125704
Title: Optimization of Memory requirement using Reinforcement Learning based on Cognitive Science in Neuromorphic VLSI Chips
Researcher: Mohammed Riyaz Ahmed
Guide(s): Sujatha B K
Keywords: Optimization
VLSI
University: Jain University
Completed Date: 07/10/2016
Abstract: Software industry is ever evolving the pressure on hardware industry for newlineincorporating more ICs in a single chip is increasing Scaling in VLSI chips is finding its limitations due to secondary effects Moreover the demand to be intelligent is posing new challenges for chip manufacturers The only way to mimic human intelligence in machines is to build the machines based on architectures of nervous system The sudden realization of need to modify the machines at architectural level to exhibit intelligence has led many investigations in biological systems newline newline
Pagination: 251p.
URI: http://hdl.handle.net/10603/125704
Appears in Departments:Department of Electronics Engineering

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01.title pages.pdfAttached File94.18 kBAdobe PDFView/Open
02.declaration.pdf51.96 kBAdobe PDFView/Open
03.certificate.pdf51.88 kBAdobe PDFView/Open
04.abstract.pdf91.19 kBAdobe PDFView/Open
05.acknowledgement.pdf119.23 kBAdobe PDFView/Open
06.contents.pdf77.61 kBAdobe PDFView/Open
08.chapter 1.pdf242.38 kBAdobe PDFView/Open
09.chapter 2.pdf932.28 kBAdobe PDFView/Open
10.chapter 3.pdf9.65 MBAdobe PDFView/Open
11.chapter 4.pdf2.1 MBAdobe PDFView/Open
12.chapter 5.pdf884.26 kBAdobe PDFView/Open
13.bibliography.pdf226.39 kBAdobe PDFView/Open


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