以人工智能为导向的框架,用于设计基于磁道结合的真随机数生成器的材料和设备
Karan P Patel1, Andrew Maicke2, Jared Arzate2
1Department of Electrical Engineering and Computer Science, University of Tennessee, Knoxville, TN, USA.
Communications engineering
|March 12, 2025
概括
人工智能优化了磁道交叉点,以实现节能计算. 这项研究使得真实随机数生成的概率设备能够实现,提高性能并最大限度地减少能源消耗.
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:
- 磁道连接 (MTJ) 对未来的节能计算至关重要.
- 设计具有特定属性的高性能MTJ是具有挑战性和耗时的.
研究的目的:
- 设计和优化旋转轨道扭矩 (SOT) 和旋转转移扭矩 (STT) MTJ模型.
- 为了利用这些MTJ作为真随机数生成 (TRNG) 的概率装置.
主要方法:
- 采用强化学习和进化优化技术.
- 多种关键设备和材料属性,以实现随机操作.
- 利用人工智能 (AI) 引导的代码设计来优化设备.
主要成果:
- 产生了能够产生随机样本的候选MTJ设备.
- 按照所需的概率分布生成样本.
- 设计设备的能源消耗最小化.
结论:
- 人工智能引导的编码设计是优化MTJ设备的有效框架.
- 这种方法有助于创建TRNG的概率装置.
- 该框架可用于优化其他设备和应用程序.
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