基于铁电容性内存的充电域内容可定位内存,用于可靠和节能的一次性学习
Zuopu Zhou1, Hongtao Zhong2, Leming Jiao1
1Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore.
Nature communications
|August 28, 2025
概括
这项研究引入了一种新的电荷域铁电容性内存CAM,用于在内存增强神经网络中更快,更可靠的哈明距离计算. 这种创新可以通过最少的数据提高学习效率.
科学领域:
- 材料科学
- 计算机工程
- 人工智能
背景情况:
- 非挥发性内容可定位记忆 (NV-CAM) 对于加速记忆增强神经网络 (MANN) 和实现少量学习至关重要.
- 现有的NV-CAM通常在当前领域运行,导致可靠,低功耗和传感友好的哈明距离 (HD) 计算存在挑战.
研究的目的:
- 通过提出一种新的电荷域计算方法来解决电流域NV-CAM的局限性.
- 引入第一个基于反转型FCM的电荷域2晶体管电容内存 (2FCM) CAM.
主要方法:
- 数据被存储在2FCM CAM结构中的设备容量.
- 该CAM直接输出HD作为线性多级电压,简化传感和降低外围成本.
- 差分操作为准确的长数据向量计算提供了对设备变化的免疫力.
主要成果:
- 使用制造的16x16 2FCM CAM阵列进行并行16位高清计算的实验演示.
- 在电荷域计算上实现了数组级记录性能.
- 验证了在内存搜索应用程序中提出的CAM的准确性和效率.
结论:
- 拟议的电荷域2FCM CAM为MANN提供了电流域NV-CAM的优越替代方案.
- 这项技术显著提高了高清计算的可靠性,功率效率和传感简单性.
- 展示的性能展示了充电域计算对于高级内存搜索应用的潜力.
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