Abstract

The objective of this project is to develop a software tool which assists in comparison of a work known as "M-GenESys: Multi Structure Genetic Algorithm based Design Space Exploration System for Integrated Scheduling, Allocation and Binding in High Level Synthesis" with another well established GA approach known as "A Genetic Algorithm for the Design Space Exploration of Data paths During High-Level Synthesis". Two sets of Software are developed based on both approaches using Microsoft visual 2005,C# language. The C# language is an object-oriented language that is aimed at enabling programmers to quickly develop a wide range of applications on the Microsoft .NET platform. The goal of C# and the .NET platform is to shorten development time by freeing the developer from worrying about several low level plumbing issues such as memory management, type safety issues, building low level libraries, array bounds checking, etc. thus allowing developers to actually spend their time and energy working on the application and business logic.

Highlights

  • Neural Networks have caught the interest of researchers for decades, especially for function approximation and classification without the need of an explicit model derivation

  • The result displays for each sport are very characteristic for a certain sport due to the fact that examples for each genre have been selected from a limited number of channels

  • A comprehensive introduction into Deep Belief Networks based on Restricted Boltzmann Machines has been provided

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Summary

Introduction

Neural Networks have caught the interest of researchers for decades, especially for function approximation and classification without the need of an explicit model derivation. Two main strains of research can be distinguished. The underlying system is modeled as exactly as possible at the expense of limited size and performance. Derivations of mathematical approximations of the real world process are in the focus of this research to understand and explain the underlying coherences. Other research tries to simplify the models to yield efficient algorithms with adequate similarity. Those models are trained to find solutions to challenging and prove their potential despite the simplification

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