Recently, due to the availability of big data and the rapid growth of computing power,
artificial intelligence (AI) has regained tremendous attention and investment. Machine …

An RRAM-based computing system (RCS) is an attractive hardware platform for
implementing neural computing algorithms. Online training for RCS enables hardware …

Resistive random access memory (RRAM) devices have been demonstrated to be a
promising solution for the implementation of a neuromorphic system with high-density …

An resistive random-access memory (RRAM)-based computing system (RCS) is an
attractive hardware platform for implementing neural computing algorithms. On-line training …

Memristors, non‐volatile switching memory platform, has recently attracted significant
interest, offering unique potential to enable the realization of human brain‐like …

This paper proposes a practical polynomial model for HfO 2 memristor fabricated in-house at
SUNY Polytechnic Institute. Although there is no shortage of memristor models in the …

Deeper and larger Neural Networks (NNs) have made breakthroughs in many fields. While
conventional CMOS-based computing platforms are hard to achieve higher energy …

Neural network (NN) computing is energyconsuming on traditional computing systems,
owing to the inherent memory wall bottleneck of the von Neumann architecture and the …

Conceptual memristors have recently gathered wider interest due to their diverse application
in non-von Neumann computing, machine learning, neuromorphic computing, and chaotic …

Resistive Random Access Memory (RRAM) and RRAM-based computing systems (RCS)
provide energy-efficient technology options for neuromorphic computing. However, the …