新兴的非易失性记忆技术在微电子的未来
Linda Katehi1, Su-In Yi1, Yuxuan Cosmi Lin1
1Electrical Engineering and Materials Science and Engineering Texas A&M University, College Station, Texas 77843, United States.
ACS omega
|July 21, 2025
概括
新兴的非易失性内存 (eNVM) 正在通过启用内存计算来彻底改变AI和ML的计算. 与传统RAM相比,这些先进的内存技术提供了更快的速度和更低的能耗.
科学领域:
- 计算机科学 计算机科学
- 材料科学 材料科学 材料科学
- 电气工程 电气工程
背景情况:
- 记忆技术对计算至关重要,从数据存储演变为AI/ML的内存计算.
- 内存计算通过在内存阵列中处理数据来提高效率,减少数据传输瓶.
- 传统的CMOS技术面临着局限性,推动了对先进的内存解决方案的需求.
研究的目的:
- 审查新兴的非易失性内存 (eNVM) 技术及其在内存计算中的潜力.
- 探索新型材料和设备架构,用于下一代的记忆.
- 讨论过渡到突触计算,以加速人工智能.
主要方法:
- 审查关于eNVM的当前文献,包括ReRAM,MRAM,FeRAM和PCM.
- 探索用于记忆应用的新型二维和有机材料.
- 从数字计算模式转向突触计算模式的分析.
主要成果:
- 在没有电源的情况下,eNVM提供数据保留,与不稳定的RAM不同,提高了系统可靠性.
- 各种eNVM类型 (ReRAM,MRAM,FeRAM,PCM) 和新型材料显示出高性能计算的前景.
- 交感计算架构可以克服人工智能开发中的重大障碍.
结论:
- 新兴的内存技术是通过内存和突触计算推进AI和ML的关键.
- 在材料和设备设计方面的持续创新对于实现eNVMs充分潜力至关重要.
- 应对当前的挑战将为计算效率和人工智能驱动的发现打开新的界限.
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