.png){kind=icon}
返回
Emerging Memory Devices for Neuromorphic Computing
.png){kind=icon}
PRE
AI
DOI：10.1002/admt.201800589.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
A neuromorphic computing system may be able to learn and perform a task on its own by interacting with its surroundings. Combining such a chip with complementary metal-oxide-semiconductor (CMOS)-based processors can potentially solve a variety of problems being faced by today's artificial intelligence (Al) systems. Although various architectures purely based on CMOS are designed to maximize the computing efficiency of Al-based applications, the most fundamental operations including matrix multiplication and convolution heavily rely on the CMOS-based multiply-accumulate units which are ultimately limited by the von Neumann bottleneck. Fortunately, many emerging memory devices can naturally perform vector matrix multiplication directly utilizing Ohm's law and Kirchhoff's law when an array of such devices is employed in a cross-bar architecture. With certain dynamics, these devices can also be used either as synapses or neurons in a neuromorphic computing system. This paper discusses various emerging nanoscale electronic devices that can potentially reshape the computing paradigm in the near future.
Keyword:
artificial synapses
emerging memory technologies
memristors
neuromorphic systems
synaptic transistors
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
6.2
论文数:
5.3K
被引数:
2.4W
机构
引用论文
Redox-Based Resistive Switching Memories - Nanoionic Mechanisms, Prospects, and Challenges基于氧化还原的电阻开关存储器-纳米离子机制，前景和挑战.png)
ADVANCED MATERIALS
ADVANCED MATERIALSIF26.8
PREAI
分享收藏Nanoelectronic Programmable Synapses Based on Phase Change Materials for Brain-Inspired ComputingNANO LETTERS
NANO LETTERSIF9.1
PREAI
分享收藏All-Solid-State Synaptic Transistor with Ultralow Conductance for Neuromorphic Computing用于神经形态计算的超低电导全固态突触晶体管
OAAI
OAAI分享收藏
查看更多内容