基于电化学随机访问内存的内置量子化与内存-晶体管传输单元,用于边缘应用程序
Zhen Yang1, Yuxiang Yang1, Baiqian Wang2
1New Cornerstone Science Laboratory, Beijing Advanced Innovation Center for Integrated Circuits, School of Integrated Circuits, Peking University, Beijing, China.
Advanced science (Weinheim, Baden-Wurttemberg, Germany)
|January 22, 2026
概括
这项研究引入了一种新的突触单元,用于神经网络中高效的内存计算. 它使低精度AI的现场重量定量成为可能,大大降低了边缘设备的能源消耗.
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:
- 使用非挥发性突触阵列进行内存计算,可以提高神经网络的能效.
- 目前的设备主要是加速矩阵-矢量乘法,由于不同的训练/推理需求,复杂化混合单元集成.
研究的目的:
- 开发一个紧的突触单元用于神经网络中低精度量子化计算.
- 允许在现场进行近似的重量定量,而不需要额外的编程,同时保留并行矩阵向量乘法 (MVM) 功能.
主要方法:
- 离子非挥发性内存-晶体管合集成用于一个紧的突触单元.
- 利用细胞的物理电气特性来实现内在的定量化功能.
主要成果:
- 在二进制神经网络中实现了与理想定量化可比的分类准确性.
- 证明支持低精度的持续学习和减轻灾难性遗忘.
- 基于ECRAM和RRAM的单元在4Mb规模的数字平台上显示出显著的能源消耗优势 (25.51×和4.84×).
结论:
- 开发的突触单元为低精度边缘训练提供了一个强大的现场定量化框架.
- 允许对二进制/三进制大语言模型进行高效的计算.
- 为更节能的人工智能硬件提供了一条途径.
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