对于神经形态电子时代的新兴记忆性人工神经元和突触设备
Jiayi Li1, Haider Abbas1, Diing Shenp Ang1
1School of Electrical and Electronics Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798. edsang@ntu.edu.sg.
Nanoscale horizons
|August 24, 2023
概括
由大脑启发的神经形态电子,为数据存储和处理提供节能解决方案. 本综述涵盖了人工神经元和突触的记忆器件的进展,并提出了对其性能的基准.
科学领域:
- 材料科学 材料科学 材料科学
- 计算机工程 计算机工程
- 神经科学是一个神经科学.
背景情况:
- 数据的指数增长需要节能计算解决方案.
- "·诺伊曼瓶"限制了传统的计算架构.
- 神经形态电子,模仿生物系统,提供内存计算能力.
研究的目的:
- 审查人工神经元和突触应用新兴记忆器件的最新进展.
- 讨论各种记忆切换机制的物理和特征.
- 为评估人工神经元和突触装置提出一个通用基准.
主要方法:
- 关于用于神经形态计算的记忆器件的最新文献的综述.
- 讨论设备物理,包括价值变化,电化学金属化,相变化,接口控制,电荷捕获,铁电道以及旋转转移扭矩.
- 建议一个基准,重点关注升的能源消耗,备用电力消耗和升时间.
主要成果:
- 确定了关键的记忆装置类型及其基础物理.
- 建立了评估人工神经元和突触性能的基准.
- 解决了挑战,并为设备优化提供了设计指南.
结论:
- 记忆器件对节能的人工神经元和突触有显著的前景.
- 拟议的基准标准有助于对神经形态器件进行标准化的评估和比较.
- 未来的研究应该专注于用于增强神经形态应用的设备内部和设备间设计.
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