A novel architecture for loser-take-all functions is proposed. Inputs and outputs of the circuit are currents, which make the circuit appropriated for low-voltage neural hardware computation. In contrast to most existing realisations the circuit does not require subtraction from a fixed reference what decreases accuracy and input dynamic. Moreover, in addition to the loser, it also outputs the minimum input current. The circuit was synthesized using a SOI (silicon on insulator) technology and optimised to work with 1.5V voltage supply showing improved speed and accuracy for a very low power consumption (Typically 5 μW per cell when the input current is 1μA)
International audienceCMOS neuron circuits used to implement neuromorphic chips require extensive ci...
Abstract This letter presents an upgraded winner‐take‐all (WTA) circuit that is capable of operating...
This dissertation explores cohesive design methodologies integrating emerging computing technologies...
This paper presents a novel low-power low-voltage analog implementation of the softmax function, wit...
A new current-mode maximum winner-take-all (Max WTA) circuit is presented. Inputs and output of the ...
An analog VLSI neural network integrated circuit is presented. It consist of a feedforward multi lay...
The CMOS circuit implementation of the feedforward neural primitives of a generic Multi Layer Percep...
take all and looser take all circuits are designed in 0.35 µm standard CMOS technology.Simulation re...
A novel, current-mode, binary-tree, asynchronous Min/Max circuit for application in nonlinear filter...
The CMOS circuit implementation of the feed forward neural primitives of a generic Multi Layer Perce...
In this paper, a mixed-signal current-mode chip is imple- mented using commercial 0.35 pm technology...
Reinforcement learning is important for machine-intelligence and neurophysiological modelling applic...
This paper deals with analog VLSI architectures addressed to the implementation of smart adaptive sy...
The design of neural network architectures is carried out using methods that optimize a particular o...
A hardware neuron that can generate a broad class of activation functions is introduced. The require...
International audienceCMOS neuron circuits used to implement neuromorphic chips require extensive ci...
Abstract This letter presents an upgraded winner‐take‐all (WTA) circuit that is capable of operating...
This dissertation explores cohesive design methodologies integrating emerging computing technologies...
This paper presents a novel low-power low-voltage analog implementation of the softmax function, wit...
A new current-mode maximum winner-take-all (Max WTA) circuit is presented. Inputs and output of the ...
An analog VLSI neural network integrated circuit is presented. It consist of a feedforward multi lay...
The CMOS circuit implementation of the feedforward neural primitives of a generic Multi Layer Percep...
take all and looser take all circuits are designed in 0.35 µm standard CMOS technology.Simulation re...
A novel, current-mode, binary-tree, asynchronous Min/Max circuit for application in nonlinear filter...
The CMOS circuit implementation of the feed forward neural primitives of a generic Multi Layer Perce...
In this paper, a mixed-signal current-mode chip is imple- mented using commercial 0.35 pm technology...
Reinforcement learning is important for machine-intelligence and neurophysiological modelling applic...
This paper deals with analog VLSI architectures addressed to the implementation of smart adaptive sy...
The design of neural network architectures is carried out using methods that optimize a particular o...
A hardware neuron that can generate a broad class of activation functions is introduced. The require...
International audienceCMOS neuron circuits used to implement neuromorphic chips require extensive ci...
Abstract This letter presents an upgraded winner‐take‐all (WTA) circuit that is capable of operating...
This dissertation explores cohesive design methodologies integrating emerging computing technologies...