Title :
Hyperbolic tangent passive resistive-type neuron
Author :
Shamsi, Jafar ; Amirsoleimani, Amirali ; Mirzakuchaki, Sattar ; Ahmade, Arash ; Alirezaee, Shahpour ; Ahmadi, Majid
Author_Institution :
Dept. of Electr. & Comput. Eng., Iran Univ. of Sci. & Technol., Tehran, Iran
Abstract :
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.
Keywords :
CMOS integrated circuits; hyperbolic equations; low-power electronics; neural nets; pattern recognition; CMOS technology; activation function; hyperbolic tangent function; neuron circuit design; passive resistive-type neuron; pattern recognition neural network; power consumption; Artificial neural networks; Biological neural networks; CMOS integrated circuits; Neurons; Pattern recognition; Power demand; Resistors; Artificial Neural Networks; CMOS; Hyperbolic Tangent Activation Function; Neuron; Pattern Recognition;
Conference_Titel :
Circuits and Systems (ISCAS), 2015 IEEE International Symposium on
Conference_Location :
Lisbon
DOI :
10.1109/ISCAS.2015.7168700