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基于RRAM的混合内存架构的单体三维集成,用于一次性学习
Yijun Li1, Jianshi Tang2,3, Bin Gao1,4
1School of Integrated Circuits, Tsinghua University, Beijing, China.
Nature communications
|November 6, 2023
概括
本研究介绍了M3D-LIME,这是一款新的3D集成芯片,它结合了互补的金属氧化物半导体 (CMOS) 逻辑与电阻随机存储器 (RRAM) 来实现高效的计算. 这种混合记忆架构在机器学习任务中实现了高精度,并显著提高了能源效率.
科学领域:
- 材料科学与工程 材料科学与工程
- 电气工程 电气工程
- 计算机科学 计算机科学
背景情况:
- 电阻随机访问存储器 (RRAM) 提供了有希望的非易失性存储器功能.
- 单立体三维集成 (M3D) 能够实现复杂的多功能芯片架构.
- 数据中心的应用程序需要高效的处理和内存解决方案.
研究的目的:
- 报告一个单立体3D混合存储器架构 (M3D-LIME) 的开发和特征.
- 为了证明互补的金属氧化物半导体 (CMOS),内存计算 (CIM) 和三元内容可定位存储器 (TCAM) 层的集成.
- 评估M3D-LIME对以数据为中心的应用程序的性能,包括一次性学习.
主要方法:
- 制造一个集成Si CMOS,基于HfAlOx的模拟RRAM,基于Ta2O5的二元RRAM和碳纳米管场效应晶体管 (CNTFET) 的多层芯片.
- 对CIM和TCAM功能进行结构分析和阵列级电气测量.
- 在M3D-LIME芯片上实现和测试一次性学习算法.
主要成果:
- 混合内存架构 (M3D-LIME) 的成功单立体3D集成.
- 证明了用于神经网络推断的功能CIM和用于模板匹配的TCAM.
- 在Omniglot数据集上实现了~96%的准确性,用于一次性学习.
- 与图形处理单元 (GPU) 相比,其能效提高了18.3倍.
结论:
- M3D-LIME展示了基于RRAM的混合内存架构在先进计算中的潜力.
- 开发的芯片使得高效的特征提取和模板匹配可用于数据密集型任务.
- 这项技术为下一代节能数据中心应用铺平了道路.
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