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 ...

This paper presents a novel Field Programmable Gate Array (FPGA) architecture for hardware implementation of Multilayer Feedforward Neural Networks (MFNNs) suitable for Digital Pre-Distortion (DPD) ...

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 ...

The binarized nBP model was implemented on Loihi hardware, extending a previous architecture with new mechanisms. Each neural network unit was represented by a spiking neuron using the current-based ...

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

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 ...

Work in computer systems covers the design and implementation of computer hardware and software, including architecture, operating systems, programming languages, security and networking.