Extending Moore's law by augmenting complementary-metal-oxide semiconductor (CMOS) transistors with emerging nanotechnologies (X) has become increasingly important. Accelerating Monte Carlo algorithms that rely on random sampling with such CMOS+X technologies could have significant impact on a large number of fields from probabilistic machine learning, optimization to quantum simulation. In this paper, we show the combination of stochastic magnetic tunnel junction (sMTJ)-based probabilistic bits (p-bits) with versatile Field Programmable Gate Arrays (FPGA) to design a CMOS + X (X = sMTJ) prototype. Our approach enables high-quality true randomness that is essential for Monte Carlo based probabilistic sampling and learning. Our heterogeneous computer successfully performs probabilistic inference and asynchronous Boltzmann learning, despite device-to-device variations in sMTJs. A comprehensive comparison using a CMOS predictive process design kit (PDK) reveals that compact sMTJ-based p-bits replace 10,000 transistors while dissipating two orders of magnitude of less energy (2 fJ per random bit), compared to digital CMOS p-bits. Scaled and integrated versions of our CMOS + stochastic nanomagnet approach can significantly advance probabilistic computing and its applications in various domains by providing massively parallel and truly random numbers with extremely high throughput and energy-efficiency.

翻译：通过将新兴纳米技术（X）与互补金属氧化物半导体（CMOS）晶体管相结合来延续摩尔定律已变得日益重要。利用此类CMOS+X技术加速依赖随机采样的蒙特卡洛算法，有望对概率机器学习、优化乃至量子模拟等众多领域产生重大影响。本文展示了一种基于随机磁隧道结（sMTJ）的概率比特（p-bit）与通用现场可编程门阵列（FPGA）相结合的设计方案，构建了CMOS+X（X=sMTJ）原型系统。该方法能够生成高质量的真随机数——这对于基于蒙特卡洛的概率采样和学习至关重要。尽管sMTJ存在器件间差异，我们的异构计算机仍成功实现了概率推理与非异步玻尔兹曼学习。通过采用CMOS预测工艺设计套件（PDK）的全面对比表明，与数字CMOS概率比特相比，紧凑型sMTJ概率比特可替代10000个晶体管，同时功耗降低两个数量级（每随机比特2 fJ）。我们提出的CMOS+随机纳米磁体方法的规模化与集成化版本，可通过提供极高吞吐量和能效的大规模并行真随机数，显著推动概率计算及其在各领域的应用发展。