基于随机磁隧道结的均值和方差可重构高斯随机数发生器
Gaussian Random Number Generator with Reconfigurable Mean and Variance using Stochastic Magnetic Tunnel Junctions
摘要：
对于机器学习和蒙特卡罗算法领域中的许多应用而言，生成具有高斯概率分布函数的高质量随机数是重要且消耗资源的计算任务。最近，基于CMOS的数字硬件架构已被探索为专门的高斯随机数发生器（GRNG）。这些基于CMOS的GRNG具有大面积，并且在其输入处需要熵源，这增加了计算成本。在这里，我们提出了一个GRNG的工作原理的玻尔兹曼定律的物理系统，从一个互连网络的热不稳定的磁性隧道结。所提出的硬件可以以千兆赫兹的速度产生多位高斯随机数，并且可以被配置为生成具有期望均值和方差的分布。所需的互连和偏置强度的分析推导，然后通过数值模拟来证明GRNG的功能。
英文摘要：
Generating high-quality random numbers with a Gaussian probability distribution function is an important and resource consuming computational task for many applications in the fields of machine learning and Monte Carlo algorithms. Recently, CMOS-based digital hardware architectures have been explored as specialized Gaussian random number generators (GRNGs). These CMOS-based GRNGs have a large area and require entropy sources at their input which increase the computing cost. Here, we propose a GRNG that works on the principle of the Boltzmann law in a physical system made from an interconnected network of thermally unstable magnetic tunnel junctions. The proposed hardware can produce multi-bit Gaussian random numbers at a gigahertz speed and can be configured to generate distributions with a desired mean and variance. An analytical derivation of the required interconnection and bias strengths is provided followed by numerical simulations to demonstrate the functionalities of the GRNG.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
