Add to ORKGThis paper presents a hardware implementation to solve the graph coloring problem (chromatic number ¢¡¤£¦ ¥ ) for arbitrary graphs using the hopfield neural network model of computation. The graph coloring problem, an NP-hard problem, has important applications in many areas including time tabling and scheduling, frequency assignment, and register allocation. The proposed algorithm has a time complexity of § ¡©¨� ¥ for a neural network with � vertices and � colors. The algorithm was implemented using VHDL and downloaded on a Field Programmable Gate Array (FPGA) device. The algorithm was simulated and tested on various graphs, all yielding optimum solutions
This article describes a novel neural stochastic model for solving graph problems. The neural system...
Living creatures pose amazing ability to learn and adapt, therefore researchers are trying to apply ...
A neural network for colouring a graph of N nodes is proposed which uses only N neurons and N<SUP>2<...
The paper discusses an effective matrix-based exact algorithm for graph colouring that is well-suite...
The paper discusses the implementation of Hopfield neural networks for solving constraint satisfacti...
This paper describes the implementation of a partially connected neural network using FPGAs (Field P...
Graphs are mathematical entities that can be used to model many real life systems. Graphs consist of...
Abstract. The usage of the FPGA (Field Programmable Gate Array) for neural network implementation pr...
A feedback neural network for solving graph coloring problem is presented. The circuit has an associ...
This project uses Verilog to design and implement a neural network with region-of-interest (ROI) and...
Neural network computing has attracted a lot of attention as it borrows the concept of human brain t...
A convolutional neural network (CNN) is a deep learning framework that is widely used in computer vi...
In contrast with analog design, digital design and implementation of any logic circuit suffer much f...
Artificial Neural Network (ANN) is very powerful to deal with signal processing, computer vision and...
This paper presents a fully parametrized framework, entirely described in VHDL, to simplify the FPGA...
This article describes a novel neural stochastic model for solving graph problems. The neural system...
Living creatures pose amazing ability to learn and adapt, therefore researchers are trying to apply ...
A neural network for colouring a graph of N nodes is proposed which uses only N neurons and N<SUP>2<...
The paper discusses an effective matrix-based exact algorithm for graph colouring that is well-suite...
The paper discusses the implementation of Hopfield neural networks for solving constraint satisfacti...
This paper describes the implementation of a partially connected neural network using FPGAs (Field P...
Graphs are mathematical entities that can be used to model many real life systems. Graphs consist of...
Abstract. The usage of the FPGA (Field Programmable Gate Array) for neural network implementation pr...
A feedback neural network for solving graph coloring problem is presented. The circuit has an associ...
This project uses Verilog to design and implement a neural network with region-of-interest (ROI) and...
Neural network computing has attracted a lot of attention as it borrows the concept of human brain t...
A convolutional neural network (CNN) is a deep learning framework that is widely used in computer vi...
In contrast with analog design, digital design and implementation of any logic circuit suffer much f...
Artificial Neural Network (ANN) is very powerful to deal with signal processing, computer vision and...
This paper presents a fully parametrized framework, entirely described in VHDL, to simplify the FPGA...
This article describes a novel neural stochastic model for solving graph problems. The neural system...
Living creatures pose amazing ability to learn and adapt, therefore researchers are trying to apply ...
A neural network for colouring a graph of N nodes is proposed which uses only N neurons and N<SUP>2<...