使用Cu0.1Te0.9/HfO2/Pt值切换记忆器实现贝叶斯网络和贝叶斯推理
In Kyung Baek1, Soo Hyung Lee1, Yoon Ho Jang1
1Department of Materials Science and Engineering, and Inter-University Semiconductor Research Center, Seoul National University Seoul 08826 Republic of Korea kevinwoo@snu.ac.kr cheolsh@snu.ac.kr.
Nanoscale advances
|May 31, 2024
概括
本研究介绍了一种新的基于memristor的贝叶斯推理电路,为传统方法提供了一种节能的替代方案. 新设计展示了精确的概率比特神经元功能,用于人工智能应用.
科学领域:
- 人工智能的人工智能
- 材料科学 材料科学 材料科学
- 计算机工程 计算机工程
背景情况:
- 贝叶斯网络和推理对人工智能至关重要,但面临着硬件实现的挑战.
- 对于贝叶斯推理而言,现有的互补金属氧化物半导体 (CMOS) 电路存在性能限制和复杂性.
研究的目的:
- 提出和演示一个新的贝叶斯网络和推理电路,利用一个挥发性的memristor.
- 为了解决贝叶斯推理传统硬件实现的局限性.
主要方法:
- 使用Cu$_{0.1}$Te$_{0.9}$/HfO$_{2}$/Pt挥发性memristor作为概率位神经元的贝叶斯网络和推理电路的开发.
- 实现具有变化学习速率的分割反逻辑,以管理设备变化和抑制错误.
主要成果:
- 可行的节点概率采样低误差,尽管memristor周期到周期的变化.
- 实现低功率 (<186nW) 和能源消耗 (441.4 fJ) 的贝叶斯推理.
- 得到了一个约为7.5 × 10$^{-4}$的正常化平均平方误差.
结论:
- 拟议的基于memristor的贝叶斯网络为人工智能应用提供了节能的解决方案.
- 这种方法显示出有可能取代传统的基于CMOS的贝叶斯估计方法.
- 使用memristor的随机计算为节能AI硬件提供了一个可行的途径.
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