In-memory learning with analog resistive switching memory: A review and perspective
In this article, we review the existing analog resistive switching memory (RSM) devices and
their hardware technologies for in-memory learning, as well as their challenges and …
their hardware technologies for in-memory learning, as well as their challenges and …
[HTML][HTML] Recent progress in analog memory-based accelerators for deep learning
We survey recent progress in the use of analog memory devices to build neuromorphic
hardware accelerators for deep learning applications. After an overview of deep learning …
hardware accelerators for deep learning applications. After an overview of deep learning …
Device and materials requirements for neuromorphic computing
Energy efficient hardware implementation of artificial neural network is challenging due
the'memory-wall'bottleneck. Neuromorphic computing promises to address this challenge by …
the'memory-wall'bottleneck. Neuromorphic computing promises to address this challenge by …
[HTML][HTML] 2D materials based optoelectronic memory: convergence of electronic memory and optical sensor
The continuous development of electron devices towards the trend of “More than Moore”
requires functional diversification that can collect data (sensors) and store (memories) and …
requires functional diversification that can collect data (sensors) and store (memories) and …
In-memory computing with emerging nonvolatile memory devices
The von Neumann bottleneck and memory wall have posed fundamental limitations in
latency and energy consumption of modern computers based on von Neumann architecture …
latency and energy consumption of modern computers based on von Neumann architecture …
[HTML][HTML] Committee machines—a universal method to deal with non-idealities in memristor-based neural networks
Artificial neural networks are notoriously power-and time-consuming when implemented on
conventional von Neumann computing systems. Consequently, recent years have seen an …
conventional von Neumann computing systems. Consequently, recent years have seen an …
Reliability aspects of binary vector-matrix-multiplications using ReRAM devices
Computation-in-memory using memristive devices is a promising approach to overcome the
performance limitations of conventional computing architectures introduced by the von …
performance limitations of conventional computing architectures introduced by the von …
LiSiOX-Based Analog Memristive Synapse for Neuromorphic Computing
J Chen, CY Lin, Y Li, C Qin, K Lu… - IEEE Electron …, 2019 - ieeexplore.ieee.org
The progress in the neuromorphic computing hinges on the development of nanoscale
analog artificial synapses. Here, we report a LiSiO X (LSO)-based memristive synapse with …
analog artificial synapses. Here, we report a LiSiO X (LSO)-based memristive synapse with …
Low conductance state drift characterization and mitigation in resistive switching memories (RRAM) for artificial neural networks
The crossbar structure of Resistive-switching random access memory (RRAM) arrays
enabled the In-Memory Computing circuits paradigm, since they imply the native …
enabled the In-Memory Computing circuits paradigm, since they imply the native …
Artificial Neural Network Based on Doped HfO2 Ferroelectric Capacitors With Multilevel Characteristics
We propose an electronic synapse based on an Al: HfO 2 metal-ferroelectric-metal (MFM)
capacitor with multi-level characteristics. The device demonstrates excellent multilevel …
capacitor with multi-level characteristics. The device demonstrates excellent multilevel …