A VLSI feedforward neural network is presented that makes use of digital weights and analog multipliers. The network is trained in a chip-in-loop fashion with a host computer implementing the training algorithm. The chip uses a serial digital weight bus implemented by a long shift register to input the weights. The inputs and outputs of the network are provided directly at pins on the chip. The training algorithm used is a parallel weight perturbation technique. Training results are shown for a 2 input, 1 output network trained with an AND function, and for a 2 input, 2 hidden unit, I output network trained with an XOR function
<div>With the advent of new technologies and advancement in medical science we are trying to process...
With the advent of new technologies and advancement in medical science we are trying to process the ...
Abstract. The usefulness of an articial analog neural network is closely bound to its trainability. ...
A VLSI feedforward neural network is presented that makes use of digital weights and analog multipli...
A VLSI feedforward neural network is presented that makes use of digital weights and analog multipli...
Two feed-forward neural-network hardware implementations are presented. The first uses analog synaps...
Analog VLSI on-chip learning Neural Networks represent a mature technology for a large number of app...
Analog VLSI implementations of artificial neural networks are usually considered efficient for the s...
Analog VLSI implementations of artificial neural networks are usually considered efficient for the s...
Results from simulations of weight perturbation as an on-chip learning scheme for analogue VLSI neur...
Results from simulations of weight perturbation as an on-chip learning scheme for analogue VLSI neur...
In this chapter, we introduce an analog chip hosting a self-learning neural network with local learn...
Presents an adaptive neural network, which uses multiplying-digital-to-analog converters (MDACs) as ...
This paper deals with analog VLSI architectures addressed to the implementation of smart adaptive sy...
Nature has evolved highly advanced systems capable of performing complex computations, adoption and ...
<div>With the advent of new technologies and advancement in medical science we are trying to process...
With the advent of new technologies and advancement in medical science we are trying to process the ...
Abstract. The usefulness of an articial analog neural network is closely bound to its trainability. ...
A VLSI feedforward neural network is presented that makes use of digital weights and analog multipli...
A VLSI feedforward neural network is presented that makes use of digital weights and analog multipli...
Two feed-forward neural-network hardware implementations are presented. The first uses analog synaps...
Analog VLSI on-chip learning Neural Networks represent a mature technology for a large number of app...
Analog VLSI implementations of artificial neural networks are usually considered efficient for the s...
Analog VLSI implementations of artificial neural networks are usually considered efficient for the s...
Results from simulations of weight perturbation as an on-chip learning scheme for analogue VLSI neur...
Results from simulations of weight perturbation as an on-chip learning scheme for analogue VLSI neur...
In this chapter, we introduce an analog chip hosting a self-learning neural network with local learn...
Presents an adaptive neural network, which uses multiplying-digital-to-analog converters (MDACs) as ...
This paper deals with analog VLSI architectures addressed to the implementation of smart adaptive sy...
Nature has evolved highly advanced systems capable of performing complex computations, adoption and ...
<div>With the advent of new technologies and advancement in medical science we are trying to process...
With the advent of new technologies and advancement in medical science we are trying to process the ...
Abstract. The usefulness of an articial analog neural network is closely bound to its trainability. ...