用磁隧道结芯片实现低功耗随机数采样，效率比现有方法高数千倍。
Energy-Efficient Sampling Using Stochastic Magnetic Tunnel Junctions
- 用室温磁性隧道结直接生成浮点随机数，跳过传统计算步骤。
- 在浮点16位均匀采样上，能效比梅森旋转算法高9721倍，比PCG高5649倍。
- 可对任意一维分布采样，适合低功耗边缘设备与大规模模拟任务。
（伪）随机采样虽广泛用于概率机器学习与马尔可夫链蒙特卡洛算法，但因计算开销过大，难以在超大规模场景下应用。本文提出一种基于室温随机磁性隧道结器件的节能型均匀Float16采样算法，直接将物理现象映射至浮点格式与均匀分布的统计特性，避免昂贵的符号运算。该方法在能效上相较最先进的梅森旋转算法最低提升9721倍，相比更节能的PCG算法提升5649倍。在此硬件与算法基础上，我们通过将任意分布分解为多个不重叠的近似均匀分布，并结合卷积与先验-似然操作，实现了无闭式解的一维分布采样。文中还测量了累积近似误差，验证了方法的有效性。
原文摘要 · Abstract (English)
(Pseudo)random sampling, a costly yet widely used method in (probabilistic) machine learning and Markov Chain Monte Carlo algorithms, remains unfeasible on a truly large scale due to unmet computational requirements. We introduce an energy-efficient algorithm for uniform Float16 sampling, utilizing a room-temperature stochastic magnetic tunnel junction device to generate truly random floating-point numbers. By avoiding expensive symbolic computation and mapping physical phenomena directly to the statistical properties of the floating-point format and uniform distribution, our approach achieves a higher level of energy efficiency than the state-of-the-art Mersenne-Twister algorithm by a minimum factor of 9721 and an improvement factor of 5649 compared to the more energy-efficient PCG algorithm. Building on this sampling technique and hardware framework, we decompose arbitrary distributions into many non-overlapping approximative uniform distributions along with convolution and prior-likelihood operations, which allows us to sample from any 1D distribution without closed-form solutions. We provide measurements of the potential accumulated approximation errors, demonstrating the effectiveness of our method.
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