Neuromorphic computing takes inspiration from the brain to create energy-efficient hardware
for information processing, capable of highly sophisticated tasks. Systems built with standard …

Neuromorphic computing uses brain-inspired principles to design circuits that can perform
computational tasks with superior power efficiency to conventional computers. Approaches …

Neuromorphic computing has emerged as a promising avenue towards building the next
generation of intelligent computing systems. It has been proposed that memristive devices …

Dense crossbar arrays of non-volatile memory (NVM) devices represent one possible path
for implementing massively-parallel and highly energy-efficient neuromorphic computing …

Neuromorphic computing holds promise for building next‐generation intelligent systems in a
more energy‐efficient way than the conventional von Neumann computing architecture …

Bioinspired hardware holds the promise of low-energy, intelligent, and highly adaptable
computing systems. Applications span from automatic classification for big data …

This work is aimed to study experimental and theoretical approaches for searching effective
local training rules for unsupervised pattern recognition by high-performance memristor …

In an increasingly data-rich world the need for developing computing systems that cannot
only process, but ideally also interpret big data is becoming continuously more pressing …

In this paper, the use of HfO 2-based oxide-based resistive memory (OxRAM) devices
operated in binary mode to implement synapses in a convolutional neural network (CNN) is …

Emerging brain-inspired architectures call for devices that can emulate the functionality of
biological synapses in order to implement new efficient computational schemes able to …