混合功能3D人工突触用于卷积和强化学习
Jiseong Im1, Jangsaeng Kim2,3, Jonghyun Ko1
1Department of Electrical and Computer Engineering and Inter-university Semiconductor Research Center, Seoul National University, Seoul 08826, Republic of Korea.
Science advances
|November 14, 2025
概括
本研究介绍了一种新的3D闪存架构,用于高效的卷积操作,增强内存计算 (CIM) 系统. 新设计提高了可靠性和能源效率,用于诸如自动驾驶等应用.
科学领域:
- 计算机工程 计算机工程
- 材料科学 材料科学 材料科学
- 人工智能的人工智能
背景情况:
- 传统的二维内存计算 (CIM) 系统在神经网络中表现出色,但在卷积神经网络 (CNN) 中效率低下.
- 需要更高效的CIM架构来支持复杂的AI任务.
- 垂直堆叠的3D闪存为新的计算范式提供了潜力.
研究的目的:
- 开发一个3D内存计算架构,以实现高效的卷积和强化学习.
- 为了减少面积的开销,并提高卷积操作中的能源效率.
- 为了证明3D卷积块 (3D CB) 和3D完全连接块 (3D FCB) 的协同集成.
主要方法:
- 设计了一个新的3D卷积块 (3D CB),利用垂直堆叠的3D闪存.
- 开发了一个兼容的3D完全连接块 (3D FCB),结构修改最小.
- 在单个晶圆上集成3D CB和3D FCB,提供完整的系统.
主要成果:
- 3D CB显著减少了空头面积,并提高了卷积的可靠性和能源效率.
- 3D CB和3D FCB展示了与无协同集成的独特功能.
- 综合系统实现了高效率的自动驾驶的精确和一致的路径规划.
结论:
- 拟议的3D CIM架构可以实现高效的卷积和强化学习.
- 这一进步为下一代内存计算技术铺平了道路.
- 该系统对节能自动驾驶应用非常有希望.
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