While CMOS scaling is currently reaching its limits in power dissipation and circuit density,
the analogy between biology and silicon is emerging as a solution to ultra-low-power signal …

Traditional computer clusters are facing a significant limitation as a result of the big data
revolution. We need efficient edge devices to bring the power of machine learning …

Recently, new emerging techniques of neuromorphic hardware render spiking neuron
networks (SNN) promising as an energy-efficient solution for artificial intelligence (AI). With …

In this paper, we propose a novel way for power amplifiers (PA) modeling using spiking
neurons. The rate of neurons firing spikes is a nonlinear function of its excitation current …

Neuromorphic computing aims to replicate the brain's function and relies on the design of
circuits inspired by biological processes. The neuron, a fundamental component of these …

The computational complexity of deep learning algorithms has given rise to significant
speed and memory challenges for the execution hardware. In energy-limited portable …

The basic concepts and techniques involved in the development and analysis of
mathematical models for individual neurons are reviewed. A spiking neuron model uses …

Different neuromorphic circuits have been proposed in recent years for low-power
implementation of spiking neurons. However, there are very few clear quantitative studies on …

Neuromorphic circuits are known for their promising ultra-low power AI applications in IoT
field. However, sub-100 mV supply voltages hamper digital-enable devices due to their non …

Recently, new emerging techniques of neuromorphic hardware render spiking neuron
networks (SNN) promising as an energy-efficient solution for artificial intelligence (AI). With …