电化学随机访问存储器:近期材料,设备和系统的进步朝着神经形态计算的方向发展
Hyunjeong Kwak1, Nayeon Kim2, Seonuk Jeon2
1Department of Materials Science and Engineering, Pohang University of Science and Technology (POSTECH), Pohang, 37673, South Korea.
Nano convergence
|February 28, 2024
概括
使用电化学随机访问存储器 (ECRAM) 的新型模拟AI硬件显著提高了能源效率. 这种带有电阻处理单元 (RPU) 的内存计算方法通过最大限度地减少数据传输来提高人工智能 (AI) 的性能.
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:
- 人工神经网络 (ANN) 和人工智能 (AI) 计算需要大量的能量,主要是由于传统架构中的数据传输,如CPU,GPU和ASIC.
- 对节能AI硬件的需求推动了对内存计算架构的研究,这些架构在内存元素中执行计算.
- 基于非挥发性内存设备的电阻处理单元 (RPU) 是模拟AI硬件加速器的关键组件,可实现并行矩阵操作.
研究的目的:
- 审查用于模拟AI硬件加速器的电化学随机访问存储器 (ECRAM) 材料的进展.
- 系统地讨论离子控制的工程策略和ECRAM操作的物理理解.
- 概述未来的研究方向,以开发节能,下一代AI硬件系统.
主要方法:
- 关于ECRAM材料进步和设备工程的文献调查.
- 对离子控制机制和电解质材料特性进行分析.
- 审查数组级演示和物理理解ECRAM操作.
主要成果:
- 电化学随机访问存储器 (ECRAM) 显示了RPU的前景,通过精确的离子运动控制实现了1000多个存储状态.
- ECRAM中的模拟状态以对称的方式更新,为高人工智能网络性能做出贡献.
- 最近在设备工程 (平面和3D) 和ECRAM物理学的理解方面取得的进展标志着重大进展.
结论:
- 对于节能模拟AI硬件加速器来说,ECRAM是一个有前途的技术.
- 在材料科学,设备工程和多学科合作领域的进一步研究对于优化基于ECRAM的AI系统至关重要.
- 未来的工作应该专注于对下一代AI硬件的电路,算法和应用程序的共同优化.
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