非挥发性记忆材料和物理建模用于内存和内传感器计算.
Shao-Xiang Go1, Kian-Guan Lim1, Tae-Hoon Lee2,3
1Department of Science, Mathematics and Technology Singapore University of Technology and Design Singapore 487372 Singapore.
Small science
|April 11, 2025
概括
内存计算为传统计算机架构的能源和时间成本提供了解决方案. 本综述探讨了人工智能应用中高效,集成计算的记忆材料.
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
- 人工智能的人工智能
背景情况:
- 传统的·诺伊曼架构由于存储器和处理单元之间的数据传输而面临能源和时间低效.
- 数据密集型应用,特别是人工智能 (AI),加剧了这些局限性,需要新的计算范式.
- 内存计算 (IMC) 作为一种非·诺伊曼策略而出现,在内存元素中执行计算以克服这些瓶.
研究的目的:
- 为内存和传感器计算提供记忆材料 (MM) 系统的概述.
- 调查MM系统在诸如脑启发计算,物理非克隆函数和随机数生成等领域的应用.
- 要总结最近的理论进展,解释MM系统的快速切换特性.
主要方法:
- 对三种原型记忆材料系统的切换特征和系统签名的审查.
- 对传感器内和内存计算的集成应用程序的调查.
- 关于快速切换在记忆材料中的结构起源的理论研究的摘要.
主要成果:
- 记忆材料通过利用它们的物理特征,为内存和内传感器计算提供了可行的途径.
- 应用范围跨越多个领域,包括人工智能,神经形态工程和安全计算.
- 理论见解正在加深对这些材料性能背后的基本机制的理解.
结论:
- 采用记忆材料的内存计算,为节能和高性能计算提供了传统架构的有希望的替代方案.
- 对MM系统及其理论基础的进一步研究对于推进人工智能和其他数据密集型应用至关重要.
- 将计算集成到传感元件中可以最大限度地减少数据的移动,为更高效,更强大的计算系统铺平了道路.
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