Deep learning neural network algorithms, including convolutional and recurrent networks, have risen to popularity in recent years. Along with this popularity has come a wide range of implementations ...

Nowadays neural networks become very useful in different fields of research such as image recognition, optimization, data analysis, classification and prediction tasks. Though, increasing complexity ...

Nowadays, more and more RF systems include switchable matching networks to decrease the impact of the environment-dependent antenna impedance on the RF front end performance. This paper reviews the ...

Hardware implementation of a MLP neural network for classifying the MNIST dataset, Computer Aided Design Course (Fall 2021), University of Tehran - ...

As machine learning algorithms – such as those that enable Siri and Alexa to recognize voice commands – grow more sophisticated, so must the hardware required to run them. Andreas Moshovos, a ...

Researchers have developed alternative learning mechanisms tailored for spiking neural networks (SNNs) and neuromorphic hardware to address these challenges. Techniques like surrogate gradients and ...

Researchers have developed a new artificial neuron device, which could reduce the computing power and hardware needed in the training of neural networks to perform tasks. The device can run neural ...