The Experts below are selected from a list of 9006 Experts worldwide ranked by ideXlab platform
Majid Ahmadi - One of the best experts on this subject based on the ideXlab platform.
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Hyperbolic Tangent passive resistive-type neuron
2015 IEEE International Symposium on Circuits and Systems (ISCAS), 2015Co-Authors: Jafar Shamsi, Amirali Amirsoleimani, Sattar Mirzakuchaki, Arash Ahmade, Shahpour Alirezaee, Majid AhmadiAbstract:In this paper, design of a passive resistive-type neuron is proposed to generate the Hyperbolic Tangent function as the activation function. The proposed resistive-type neuron has the advantage of not needing any biasing voltage and therefore its power consumption is low. The neuron circuit is designed and simulated in 180 nm CMOS technology. The proposed neuron shows a good approximation with maximum error and average error from the ideal Hyperbolic Tangent function by 19.7% and 6.88% respectively. The power consumption of the proposed neuron is 62.5 μW while the standby power is zero. Also the proposed neuron is applied in a large neural network and the results shows good functionality. The pattern recognition neural network implemented using the proposed neuron is consumed 295 μW power that is approximately 59.86% less than the same network proposed with the previous analog Hyperbolic Tangent designed neuron.
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ISCAS - Hyperbolic Tangent passive resistive-type neuron
2015 IEEE International Symposium on Circuits and Systems (ISCAS), 2015Co-Authors: Jafar Shamsi, Amirali Amirsoleimani, Sattar Mirzakuchaki, Arash Ahmade, Shahpour Alirezaee, Majid AhmadiAbstract:In this paper, design of a passive resistive-type neuron is proposed to generate the Hyperbolic Tangent function as the activation function. The proposed resistive-type neuron has the advantage of not needing any biasing voltage and therefore its power consumption is low. The neuron circuit is designed and simulated in 180 nm CMOS technology. The proposed neuron shows a good approximation with maximum error and average error from the ideal Hyperbolic Tangent function by 19.7% and 6.88% respectively. The power consumption of the proposed neuron is 62.5 μW while the standby power is zero. Also the proposed neuron is applied in a large neural network and the results shows good functionality. The pattern recognition neural network implemented using the proposed neuron is consumed 295 μW power that is approximately 59.86% less than the same network proposed with the previous analog Hyperbolic Tangent designed neuron.
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Efficient hardware implementation of the Hyperbolic Tangent sigmoid function
2009 IEEE International Symposium on Circuits and Systems, 2009Co-Authors: Ashkan Hosseinzadeh Namin, Karl Leboeuf, Roberto Muscedere, Huapeng Wu, Majid AhmadiAbstract:Efficient implementation of the activation function is important in the hardware design of artificial neural networks. Sigmoid, and Hyperbolic Tangent sigmoid functions are the most widely used activation functions for this purpose. In this paper, we present a simple and efficient architecture for digital hardware implementation of the Hyperbolic Tangent sigmoid function. The proposed method employs a piecewise linear approximation as a foundation, and further improves the results using a lookup table. Our design proves to be more efficient considering area times delay as a performance metric when compared to similar proposals. VLSI implementation of the proposed design using a 0.18 mum CMOS process is also presented, which shows a 35% improvement over similar recently published architectures.
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ISCAS - Efficient hardware implementation of the Hyperbolic Tangent sigmoid function
2009 IEEE International Symposium on Circuits and Systems, 2009Co-Authors: Ashkan Hosseinzadeh Namin, Karl Leboeuf, Roberto Muscedere, Huapeng Wu, Majid AhmadiAbstract:Efficient implementation of the activation function is important in the hardware design of artificial neural networks. Sigmoid, and Hyperbolic Tangent sigmoid functions are the most widely used activation functions for this purpose. In this paper, we present a simple and efficient architecture for digital hardware implementation of the Hyperbolic Tangent sigmoid function. The proposed method employs a piecewise linear approximation as a foundation, and further improves the results using a lookup table. Our design proves to be more efficient considering area × delay as a performance metric when compared to similar proposals. VLSI implementation of the proposed design using a 0.18µm CMOS process is also presented, which shows a 35% improvement over similar recently published architectures.
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High Speed VLSI Implementation of the Hyperbolic Tangent Sigmoid Function
2008 Third International Conference on Convergence and Hybrid Information Technology, 2008Co-Authors: Karl Leboeuf, Ashkan Hosseinzadeh Namin, Roberto Muscedere, Huapeng Wu, Majid AhmadiAbstract:The Hyperbolic Tangent function is commonly used as the activation function in artificial neural networks. In this work two different hardware implementations for the Hyperbolic Tangent function are proposed. Both methods are based on the approximation of the function rather than calculating it, since it has exponential nature. The first method uses a lookup table to approximate the function, while the second method reduces the size of the table by using range addressable decoding as opposed to the classic decoding scheme. Hardware synthesis results show the proposed methods perform significantly faster, and use less area compared to other similar methods with the same amount of error.
A. Artes-rodriguez - One of the best experts on this subject based on the ideXlab platform.
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Support vector classifier with Hyperbolic Tangent penalty function
2000 IEEE International Conference on Acoustics Speech and Signal Processing. Proceedings (Cat. No.00CH37100), 2000Co-Authors: F. Perez-cruz, A. Navia-vazquez, P.l. Alarcon-diana, A. Artes-rodriguezAbstract:The support vector classifier is a new tool to solve classification problems, giving the classification boundary as a linear combination of the training samples. In non-separable problems with highly overlapped classes, the achieved classifiers are oversized. In this paper, we proposed to change the support vector classifier penalty function by an Hyperbolic Tangent one, obtaining as a result of the training phase a reduced support vector classifier with the same performance as the original one.
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ICASSP - Support vector classifier with Hyperbolic Tangent penalty function
2000 IEEE International Conference on Acoustics Speech and Signal Processing. Proceedings (Cat. No.00CH37100), 2000Co-Authors: F. Perez-cruz, A. Navia-vazquez, P.l. Alarcon-diana, A. Artes-rodriguezAbstract:The support vector classifier is a new tool to solve classification problems, giving the classification boundary as a linear combination of the training samples. In non-separable problems with highly overlapped classes, the achieved classifiers are oversized. In this paper, we proposed to change the support vector classifier penalty function by an Hyperbolic Tangent one, obtaining as a result of the training phase a reduced support vector classifier with the same performance as the original one.
Jafar Shamsi - One of the best experts on this subject based on the ideXlab platform.
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Hyperbolic Tangent passive resistive-type neuron
2015 IEEE International Symposium on Circuits and Systems (ISCAS), 2015Co-Authors: Jafar Shamsi, Amirali Amirsoleimani, Sattar Mirzakuchaki, Arash Ahmade, Shahpour Alirezaee, Majid AhmadiAbstract:In this paper, design of a passive resistive-type neuron is proposed to generate the Hyperbolic Tangent function as the activation function. The proposed resistive-type neuron has the advantage of not needing any biasing voltage and therefore its power consumption is low. The neuron circuit is designed and simulated in 180 nm CMOS technology. The proposed neuron shows a good approximation with maximum error and average error from the ideal Hyperbolic Tangent function by 19.7% and 6.88% respectively. The power consumption of the proposed neuron is 62.5 μW while the standby power is zero. Also the proposed neuron is applied in a large neural network and the results shows good functionality. The pattern recognition neural network implemented using the proposed neuron is consumed 295 μW power that is approximately 59.86% less than the same network proposed with the previous analog Hyperbolic Tangent designed neuron.
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ISCAS - Hyperbolic Tangent passive resistive-type neuron
2015 IEEE International Symposium on Circuits and Systems (ISCAS), 2015Co-Authors: Jafar Shamsi, Amirali Amirsoleimani, Sattar Mirzakuchaki, Arash Ahmade, Shahpour Alirezaee, Majid AhmadiAbstract:In this paper, design of a passive resistive-type neuron is proposed to generate the Hyperbolic Tangent function as the activation function. The proposed resistive-type neuron has the advantage of not needing any biasing voltage and therefore its power consumption is low. The neuron circuit is designed and simulated in 180 nm CMOS technology. The proposed neuron shows a good approximation with maximum error and average error from the ideal Hyperbolic Tangent function by 19.7% and 6.88% respectively. The power consumption of the proposed neuron is 62.5 μW while the standby power is zero. Also the proposed neuron is applied in a large neural network and the results shows good functionality. The pattern recognition neural network implemented using the proposed neuron is consumed 295 μW power that is approximately 59.86% less than the same network proposed with the previous analog Hyperbolic Tangent designed neuron.
S. Haykin - One of the best experts on this subject based on the ideXlab platform.
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The separability theory of Hyperbolic Tangent kernels and support vector machines for pattern classification
1999 IEEE International Conference on Acoustics Speech and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258), 1999Co-Authors: M. Sellathurai, S. HaykinAbstract:A new theory is developed for the feature spaces of Hyperbolic Tangent used as an activation kernel for non-linear support vector machines. The theory developed herein is based on the distinct features of Hyperbolic geometry, which leads to an interesting geometrical interpretation of the higher-dimensional feature spaces of neural networks using Hyperbolic Tangent as the activation function. The new theory is used to explain the separability of Hyperbolic Tangent kernels where we show that the separability is possible only for a certain class of Hyperbolic kernels. Simulation results are given supporting the separability theory.
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ICASSP - The separability theory of Hyperbolic Tangent kernels and support vector machines for pattern classification
1999 IEEE International Conference on Acoustics Speech and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258), 1999Co-Authors: M. Sellathurai, S. HaykinAbstract:A new theory is developed for the feature spaces of Hyperbolic Tangent used as an activation kernel for non-linear support vector machines. The theory developed herein is based on the distinct features of Hyperbolic geometry, which leads to an interesting geometrical interpretation of the higher-dimensional feature spaces of neural networks using Hyperbolic Tangent as the activation function. The new theory is used to explain the separability of Hyperbolic Tangent kernels where we show that the separability is possible only for a certain class of Hyperbolic kernels. Simulation results are given supporting the separability theory.
F. Perez-cruz - One of the best experts on this subject based on the ideXlab platform.
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Support vector classifier with Hyperbolic Tangent penalty function
2000 IEEE International Conference on Acoustics Speech and Signal Processing. Proceedings (Cat. No.00CH37100), 2000Co-Authors: F. Perez-cruz, A. Navia-vazquez, P.l. Alarcon-diana, A. Artes-rodriguezAbstract:The support vector classifier is a new tool to solve classification problems, giving the classification boundary as a linear combination of the training samples. In non-separable problems with highly overlapped classes, the achieved classifiers are oversized. In this paper, we proposed to change the support vector classifier penalty function by an Hyperbolic Tangent one, obtaining as a result of the training phase a reduced support vector classifier with the same performance as the original one.
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ICASSP - Support vector classifier with Hyperbolic Tangent penalty function
2000 IEEE International Conference on Acoustics Speech and Signal Processing. Proceedings (Cat. No.00CH37100), 2000Co-Authors: F. Perez-cruz, A. Navia-vazquez, P.l. Alarcon-diana, A. Artes-rodriguezAbstract:The support vector classifier is a new tool to solve classification problems, giving the classification boundary as a linear combination of the training samples. In non-separable problems with highly overlapped classes, the achieved classifiers are oversized. In this paper, we proposed to change the support vector classifier penalty function by an Hyperbolic Tangent one, obtaining as a result of the training phase a reduced support vector classifier with the same performance as the original one.