Add to ORKGThis project presented a backpropagation neural network on FPGA which can conduct inference and training processes for linear and non-linear problems. The network structure chosen contains 3 input nodes, one hidden layer with three neuron units and 1 output node. In addition, this project compares the training time between MATLAB and FPGA. The FPGA can achieve a much shorter training time owing to architecture advantage and computation data type simplification. In the end, the result of the neural network is displayed on the LEDs on the FPGA board. Keywords:Master of Science (Electronics
Living creatures pose amazing ability to learn and adapt, therefore researchers are trying to apply ...
Neural networks are employed in a large variety of practical contexts. However, the majority of such...
In this paper a hardware implementation of a neural network using Field Programmable Gate Arrays (FP...
This work presents a parametrizable design of a neural network on an FPGA, being trained previously ...
In this paper, a design method of neural networks based on VHDL hardware description language, and F...
Abstract—The well known backpropagation learning algo-rithm is implemented in a FPGA board and a mic...
This paper describes a new platform for FPGA implementation of the multilayer perceptron (MLP) back ...
The objectives are to investigate the use of FPGA-based reconfigurable architecture to implement art...
The objectives are to investigate the use of FPGA-based reconfigurable architecture to implement art...
Artificial Neural Network (ANN) is very powerful to deal with signal processing, computer vision and...
The first successful implementation of Artificial Neural Networks (ANNs) was published a little over...
Abstract. The usage of the FPGA (Field Programmable Gate Array) for neural network implementation pr...
Very often complex transfer functions are needed to be implemented in ASIC for faster or real-time a...
Neural network computing has attracted a lot of attention as it borrows the concept of human brain t...
Article dans revue scientifique avec comité de lecture.The use of reprogrammable hardware devices ma...
Living creatures pose amazing ability to learn and adapt, therefore researchers are trying to apply ...
Neural networks are employed in a large variety of practical contexts. However, the majority of such...
In this paper a hardware implementation of a neural network using Field Programmable Gate Arrays (FP...
This work presents a parametrizable design of a neural network on an FPGA, being trained previously ...
In this paper, a design method of neural networks based on VHDL hardware description language, and F...
Abstract—The well known backpropagation learning algo-rithm is implemented in a FPGA board and a mic...
This paper describes a new platform for FPGA implementation of the multilayer perceptron (MLP) back ...
The objectives are to investigate the use of FPGA-based reconfigurable architecture to implement art...
The objectives are to investigate the use of FPGA-based reconfigurable architecture to implement art...
Artificial Neural Network (ANN) is very powerful to deal with signal processing, computer vision and...
The first successful implementation of Artificial Neural Networks (ANNs) was published a little over...
Abstract. The usage of the FPGA (Field Programmable Gate Array) for neural network implementation pr...
Very often complex transfer functions are needed to be implemented in ASIC for faster or real-time a...
Neural network computing has attracted a lot of attention as it borrows the concept of human brain t...
Article dans revue scientifique avec comité de lecture.The use of reprogrammable hardware devices ma...
Living creatures pose amazing ability to learn and adapt, therefore researchers are trying to apply ...
Neural networks are employed in a large variety of practical contexts. However, the majority of such...
In this paper a hardware implementation of a neural network using Field Programmable Gate Arrays (FP...