Memristors have attracted increasing attention due to their tremendous potential to
accelerate data-centric computing systems. The dynamic reconfiguration of memristive …

Sneak path current generated by adjacent cells in three-dimensional (3D) memristor arrays
must be curbed while securing the multi-bit storage capability of each cell to aid in the cost …

Spiking encoded stochastic neural network is believed to be energy efficient and biologically
plausible and an increasing effort has been made recently to translate its great cognitive …

Neuromorphic computing has recently emerged as a potential alternative to the
conventional von Neumann computer paradigm, which is inherently limited due to its …

Neuromorphic computing is a critical future technology for the computing industry, but it has
yet to achieve its promise and has struggled to establish a cohesive research community. A …

For more than five decades, the flexibility of the von Neumann architecture—in which data
from discrete memory units arrive at dedicated compute units as both operations and …

Stochastic neurons are efficient hardware accelerators for solving a large variety of
combinatorial optimization problems.'Binary'stochastic neurons (BSN) are those whose …

Recent advancements in artificial intelligence systems have been propelled spectacularly by
the progress in machine learning techniques, particularly deep neural networks and spiking …

This study presents findings that demonstrate the possibility of simplifying neural networks
by inducing multifunctionality through separate manipulation within a single material …

Liquid State Machines are brain inspired spiking neural networks (SNNs) with random
reservoir connectivity and bio-mimetic neuronal and synaptic models. Reservoir computing …