In this brief, we present a new circuit technique to generate the sigmoid neuron activation function (NAF) and its derivative (DNAF). The circuit makes use of transistor asymmetry in cross-coupled differential pair to obtain the derivative. The asymmetry is introduced through external control signal, as and when required. This results in the efficient utilization of the hard-ware by realizing NAF and DNAF using the same building blocks. The operation of the circuit is presented in the subthreshold region for ultra low-power applications. The proposed circuit has been experimentally prototyped and characterized as a proof of concept on the 1.5-mum AMI technology
Previous studies show that the conventional pair-based form of STDP (PSTDP), is not able to account ...
Hardware implementations of spiking neural networks offer promising solutions for a wide set of task...
This paper discusses the artificial neural network (ANN) implementation into a field programmable ga...
In this brief, we present a new circuit technique to generate the sigmoid neuron activation functio...
A simple neuron circuit is presented which can generate the sigmoid neuron activiation function (NAF...
A new performance metric, Peak-Error Ratio (PER) has been presented to benchmark the performance of...
International audienceIn this paper, we propose to implement the sigmoid function, which will serve ...
CMOS implementation of a new kind of neuron activation function with a current mode circuit is intro...
A CMOS Satlin/Sigmoid/Gaussian/triangular Basis functions computation circuit suitable for analog ne...
The design of a new digitally programmable analogue circuit well suited for the implementation of se...
A CMOS Satlin/Sigmoid/Gaussian/Triangular Basis functions computation circuit suitable for analog ne...
We demonstrate a programmable analog opto-electronic (OE) circuit that can be configured to provide ...
In this paper we propose a low-error approximation of the sigmoid function and hyperbolic tangent, w...
In this paper a novel current-mode CMOS differential pair which uses the translinear principle is su...
We present a novel methodology to enable control of a neuromorphic circuit in close analogy with the...
Previous studies show that the conventional pair-based form of STDP (PSTDP), is not able to account ...
Hardware implementations of spiking neural networks offer promising solutions for a wide set of task...
This paper discusses the artificial neural network (ANN) implementation into a field programmable ga...
In this brief, we present a new circuit technique to generate the sigmoid neuron activation functio...
A simple neuron circuit is presented which can generate the sigmoid neuron activiation function (NAF...
A new performance metric, Peak-Error Ratio (PER) has been presented to benchmark the performance of...
International audienceIn this paper, we propose to implement the sigmoid function, which will serve ...
CMOS implementation of a new kind of neuron activation function with a current mode circuit is intro...
A CMOS Satlin/Sigmoid/Gaussian/triangular Basis functions computation circuit suitable for analog ne...
The design of a new digitally programmable analogue circuit well suited for the implementation of se...
A CMOS Satlin/Sigmoid/Gaussian/Triangular Basis functions computation circuit suitable for analog ne...
We demonstrate a programmable analog opto-electronic (OE) circuit that can be configured to provide ...
In this paper we propose a low-error approximation of the sigmoid function and hyperbolic tangent, w...
In this paper a novel current-mode CMOS differential pair which uses the translinear principle is su...
We present a novel methodology to enable control of a neuromorphic circuit in close analogy with the...
Previous studies show that the conventional pair-based form of STDP (PSTDP), is not able to account ...
Hardware implementations of spiking neural networks offer promising solutions for a wide set of task...
This paper discusses the artificial neural network (ANN) implementation into a field programmable ga...