在memristor交叉条数组中实现的随机神经模糊系统
Tuo Shi1,2, Hui Zhang2, Shiyu Cui2
1State Key Lab of Fabrication Technologies for Integrated Circuits, Institute of Microelectronics, Chinese Academy of Sciences, Beijing 100029, China.
Science advances
|March 22, 2024
概括
这项研究为人工智能引入了基于memristor的新型神经模糊硬件. 与传统系统相比,这种AI硬件提供了更高的速度和能源效率.
科学领域:
- 材料科学 材料科学 材料科学
- 人工智能的人工智能
- 神经科学是一个神经科学.
背景情况:
- 对可解释和适应的神经网络的需求日益增长.
- 由于复杂性,实施神经符号AI硬件的挑战.
- 需要高效,强大的人工智能系统.
研究的目的:
- 为了展示基于memristor的神经模糊硬件系统.
- 提高人工智能的吞吐量和能源效率.
- 提高人工智能系统对新环境的适应性.
主要方法:
- 在数组拓结构中使用了TiN/TaO/HfO/TiN记忆器芯片.
- 运用物理定律进行计算,并利用内在的memristor可变性.
- 实施了混合现场培训策略,以尽量减少错误.
主要成果:
- 与基于的同行相比,实现了优越的吞吐量和能源效率.
- 演示了比深度学习快6.6倍的融合速度和~6倍的误差.
- 显示了比FPGAs更高的两个数量级的能量效率.
结论:
- 基于memristor的神经模糊硬件显著提升了神经形态计算能力.
- 这种方法提高了AI的适应性,并减少了培训错误.
- 开发的硬件为高性能,节能的人工智能提供了一个有前途的解决方案.
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