Add to ORKGA new performance metric, Peak-Error Ratio (PER) has been presented to benchmark the performance of a class of neuron circuits to realize neuron activation function (NAF) and its derivative (DNAF). Neuron circuits, biased in subthreshold region, based on the asymmetric cross-coupled differential pair configuration and conventional configuration of applying small external offset voltage at the input have been compared on the basis of PER. It is shown that the technique of using transistor asymmetry in a cross-coupled differential pair performs on-par with that of applying external offset voltage. The neuron circuits have been experimentally prototyped and characterized as a proof of concept on the 1.5 mu m AMI technology
Artificial Neural Networks are powerful computational tools with many diverse applications in a vari...
As the integrated circuit (IC) technology advances into smaller nanometre feature sizes, a fixed-err...
In this paper, we describe a methodical approach for reducing errors due to mismatch in neuron circu...
A new performance metric, Peak-Error Ratio (PER) has been presented to benchmark the performance of...
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...
The neuron MOS transistor is a recently discovered device which is capable of executing a weighted s...
Neuromorphic computing describes the use of electrical circuits to mimic biological architecture pre...
Improved structures for neuron MOSFETs, which can execute a weighted summation of multiple input sig...
In this thesis, we describe a methodical approach for reducing errors due to mismatch in neuron circ...
Neuro-inspired electronics are being designed to efficiently process complex information and accompl...
International audienceAs Moore's law reaches its end, traditional computing technology based on the ...
In the biological nervous system, large neuronal populations work collaboratively to encode sensory ...
The sustainability of ever more sophisticated artificial intelligence relies on the continual develo...
As the integrated circuit (IC) technology advances into smaller nanometre feature sizes, a fixed-err...
Artificial Neural Networks are powerful computational tools with many diverse applications in a vari...
As the integrated circuit (IC) technology advances into smaller nanometre feature sizes, a fixed-err...
In this paper, we describe a methodical approach for reducing errors due to mismatch in neuron circu...
A new performance metric, Peak-Error Ratio (PER) has been presented to benchmark the performance of...
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...
The neuron MOS transistor is a recently discovered device which is capable of executing a weighted s...
Neuromorphic computing describes the use of electrical circuits to mimic biological architecture pre...
Improved structures for neuron MOSFETs, which can execute a weighted summation of multiple input sig...
In this thesis, we describe a methodical approach for reducing errors due to mismatch in neuron circ...
Neuro-inspired electronics are being designed to efficiently process complex information and accompl...
International audienceAs Moore's law reaches its end, traditional computing technology based on the ...
In the biological nervous system, large neuronal populations work collaboratively to encode sensory ...
The sustainability of ever more sophisticated artificial intelligence relies on the continual develo...
As the integrated circuit (IC) technology advances into smaller nanometre feature sizes, a fixed-err...
Artificial Neural Networks are powerful computational tools with many diverse applications in a vari...
As the integrated circuit (IC) technology advances into smaller nanometre feature sizes, a fixed-err...
In this paper, we describe a methodical approach for reducing errors due to mismatch in neuron circu...