用AI协同设计磁随机数生成器，兼顾性能与能效。
AI-Guided Codesign Framework for Novel Material and Device Design applied to MTJ-based True Random Number Generators
- 结合强化学习与进化优化，自动调整材料与器件参数
- 生成满足指定分布的随机输出，同时降低能耗
- 适合新型低功耗随机数生成器研发人员参考
新型器件与计算范式是实现高效、高性能未来计算系统的关键。然而，为新应用设计器件通常耗时且繁琐。本文研究了自旋轨道力矩与自旋转移力矩磁隧道结模型在真随机数生成中的设计与优化。我们利用强化学习和进化优化方法，调节不同器件模型的关键材料与器件特性，以实现随机工作状态。所提出的AI引导协同设计方法生成了多种候选器件，能够生成符合目标概率分布的随机样本，同时最小化器件能耗。
原文摘要 · Abstract (English)
Novel devices and novel computing paradigms are key for energy efficient, performant future computing systems. However, designing devices for new applications is often time consuming and tedious. Here, we investigate the design and optimization of spin orbit torque and spin transfer torque magnetic tunnel junction models as the probabilistic devices for true random number generation. We leverage reinforcement learning and evolutionary optimization to vary key device and material properties of the various device models for stochastic operation. Our AI guided codesign methods generated different candidate devices capable of generating stochastic samples for a desired probability distribution, while also minimizing energy usage for the devices.
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