一个用于训练和推理的铁电-memristor内存
Michele Martemucci1,2, François Rummens2, Yannick Malot2
1Université Grenoble Alpes, CEA-Leti, Grenoble, France.
Nature electronics
|October 29, 2025
概括
研究人员开发了一种用于边缘人工智能 (AI) 的新型统一记忆堆. 这种混合存储器可以实现高效的推断和学习,克服当前AI硬件技术的局限性.
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:
- 边缘人工智能 (AI) 需要节能推断和学习能力.
- 现有的内存技术在耐用性,编程能量和读取过程方面面临限制,阻碍了AI的发展.
- 需要一个统一的存储器解决方案来结合不同类型的存储器在AI应用中的优势.
研究的目的:
- 开发一种新的统一内存堆,集成memristor和铁电电容器的功能.
- 解决当前存储技术对节能边缘AI的局限性.
- 通过使用混合记忆阵列来演示芯片上学习解决方案.
主要方法:
- 使用添加氧化和清理层制造统一的内存堆.
- 集成到一个互补的金属氧化物半导体 (CMOS) 后端的线路过程中.
- 创建一个 18,432 设备的混合阵列与芯片上的 CMOS 外围电路.
主要成果:
- 统一的内存堆成功地作为一个memristor和一个铁电电容器.
- 一个混合阵列证明了在没有数字对模拟转换器的情况下,在内存类型之间有效的重量转移.
- 在芯片上学习解决方案实现了与基准软件模型的竞争性性能.
结论:
- 开发的统一内存堆为节能边缘AI提供了一个有前途的解决方案.
- 这种混合内存架构克服了现有AI硬件技术的关键局限性.
- 展示的芯片上学习验证了这种方法对未来人工智能系统的潜力.
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