In-memory learning with analog resistive switching memory: A review and perspective

Y Xi, B Gao, J Tang, A Chen, MF Chang… - Proceedings of the …, 2020 - ieeexplore.ieee.org
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 …

[HTML][HTML] Recent progress in analog memory-based accelerators for deep learning

H Tsai, S Ambrogio, P Narayanan… - Journal of Physics D …, 2018 - iopscience.iop.org
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 …

Device and materials requirements for neuromorphic computing

R Islam, H Li, PY Chen, W Wan… - Journal of Physics …, 2019 - new.iopscience.iop.org
Energy efficient hardware implementation of artificial neural network is challenging due
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

F Zhou, J Chen, X Tao, X Wang, Y Chai - Research, 2019 - spj.science.org
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 …

In-memory computing with emerging nonvolatile memory devices

C Cheng, PJ Tiw, Y Cai, X Yan, Y Yang… - Science China Information …, 2021 - Springer
The von Neumann bottleneck and memory wall have posed fundamental limitations in
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

D Joksas, P Freitas, Z Chai, WH Ng, M Buckwell… - Nature …, 2020 - nature.com
Artificial neural networks are notoriously power-and time-consuming when implemented on
conventional von Neumann computing systems. Consequently, recent years have seen an …

Reliability aspects of binary vector-matrix-multiplications using ReRAM devices

C Bengel, J Mohr, S Wiefels, A Singh… - Neuromorphic …, 2022 - iopscience.iop.org
Computation-in-memory using memristive devices is a promising approach to overcome the
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 …

Low conductance state drift characterization and mitigation in resistive switching memories (RRAM) for artificial neural networks

A Baroni, A Glukhov, E Pérez, C Wenger… - … on Device and …, 2022 - ieeexplore.ieee.org
The crossbar structure of Resistive-switching random access memory (RRAM) arrays
enabled the In-Memory Computing circuits paradigm, since they imply the native …

Artificial Neural Network Based on Doped HfO2 Ferroelectric Capacitors With Multilevel Characteristics

Q Zheng, Z Wang, N Gong, Z Yu… - IEEE Electron …, 2019 - ieeexplore.ieee.org
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 …