{"id":16875,"date":"2023-03-03T04:50:52","date_gmt":"2023-03-03T04:50:52","guid":{"rendered":"https:\/\/file.currentschoolnews.com\/?post_type=product&p=16875"},"modified":"2023-03-03T10:25:24","modified_gmt":"2023-03-03T10:25:24","slug":"hardware-emulation-study-of-neuronal-processing-in-cortex-for-pattern-recognition","status":"publish","type":"product","link":"https:\/\/pastexamquestions.com\/product\/hardware-emulation-study-of-neuronal-processing-in-cortex-for-pattern-recognition\/","title":{"rendered":"Hardware Emulation Study of Neuronal Processing in Cortex for Pattern Recognition"},"content":{"rendered":"

Download Hardware Emulation Study of Neuronal Processing in Cortex for Pattern Recognition<\/strong><\/span>. Computer Science students who are writing their projects can get this material to aid their research work.<\/span><\/span><\/p>\n

Abstract<\/b><\/span><\/h2>\n

Artificial Neural network (ANN) is an area of computing that is modeled after the neural network of the biological brain and over the last few decades, has experienced huge success in its application in areas such as business, Medicine, Industry, Automotive, Astronomy, Finance, etc.<\/p>\n

Since Neural Networks are inherently parallel architectures, there have been several earlier researches to build custom ASIC based systems that include multiple parallel processing units.<\/p>\n

However, these ASIC based systems suffered from several limitations such as the ability to run only specific algorithms and limitations on the size of a network.<\/p>\n

Recently, much work has focused on implementing artificial neural networks on reconfigurable computing platforms. Reconfigurable computing allows to increasing the processing density beyond that provided by general-purpose computing systems.<\/p>\n

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Introduction<\/span><\/span><\/strong><\/span><\/h2>\n

Moore\u2019s law predicted that the number of transistors on a dense integrated circuit doubles every two years(Moore, 1975). So far, this has been true, but it is only a matter of time before this circuit will max out, and this is because further increasing the number of transistors on it will make it consume more power, overheat and become impossible to cool.<\/p>\n

Again, it is difficult to get the conventional computer with Von Neumann architecture to perform operations like understanding human languages, recognizing objects, learning to dance, etc. activities the human brain does very easily.<\/p>\n

The human brain is not good at arithmetic operations, but it does well in operations that involves processing continuous streams of data from the environment and can do it very quickly.<\/p>\n

So, to build a computer that will be able to carry out these activities, a computing paradigm called artificial neural network which mimics the biological brain was adopted(Abdallah, 2017).<\/p>\n

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