神经形态计算中的突触可塑性的闪存:一篇评论
Jisung Im1, Sangyeon Pak2, Sung-Yun Woo1
1School of Electronic and Electrical Engineering, Kyungpook National University, Daegu 41566, Republic of Korea.
Biomimetics (Basel, Switzerland)
|February 25, 2025
概括
使用闪存的神经形态电子产品提供了节能的内存计算. 本综述探讨了针对优化神经网络应用的NOR,AND和NAND闪存进步.
科学领域:
- 神经形态工程的神经形态工程
- 计算机科学 计算机科学
- 材料科学 材料科学 材料科学
背景情况:
- 数据的指数增长需要节能解决方案来存储和处理.
- 神经形态电子,模仿生物神经系统,提供了一条超越·诺伊曼瓶的道路.
- 带有突触器件的内存计算承诺减少计算任务的能源消耗.
研究的目的:
- 审查使用NOR,AND和NAND闪存的神经形态计算的最新进展.
- 提供基于闪存的神经形态计算的全面概述.
- 突出闪存在优化神经网络的能源效率方面的潜力.
主要方法:
- 在突触器件中分析多位非挥发性和生物灵感特征.
- 在乘法和积累运算中探索欧姆定律的能量的减少.
- 对数组架构,操作方法和NOR,AND和NAND闪存的电气性能进行审查.
主要成果:
- 闪存,特别是NOR,AND和NAND类型,在神经形态系统中显示出大规模数据存储的巨大潜力.
- 基于闪存的突触设备可以有效地减少基本计算操作中的能源消耗.
- 集成闪存可以有效地实现各种神经网络设计.
结论:
- 闪存是节能神经形态计算和内存处理的竞争性技术.
- 了解闪存特征对于优化其在各种神经网络架构中的应用至关重要.
- 本综述为在各种应用中推进基于闪存的神经形态计算提供了见解.
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