Monte Carlo particle transport problems play a vital role in scientific computing, but solving them on exiting von Neumann architectures suffers from random branching and irregular memory access, causing computing inefficiency due to a fundamental mismatch between stochastic algorithms and deterministic hardware. To bridge this gap, we propose MCPT-Solver, a spin-based hardware true random number generator (TRNG) with tunable output probability enabled by a Bayesian inference network architecture. It is dedicated for efficiently solving stochastic applications including Monte Carlo particle transport problems. First, we leverage the stochastic switching property of spin devices to provide a high-quality entropy source for the TRNG and achieve high generating throughput and process-voltage-temperature tolerance through optimized control logic and write mechanism designs. Next, we propose a hardware Bayesian inference network to enable probability-tunable random number outputs. Finally, we present a system-level simulation framework to evaluate MCPT-Solver. Experimental results show that MCPT-Solver achieves a mean squared error of 7.6e-6 for solving transport problems while demonstrating a dramatic acceleration effect over general-purpose processors. Additionally, the MCPT-Solver's throughput reaches 185 Mb/s with an area of 27.8 um2/bit and energy consumption of 8.6 pJ/bit, making it the first spin-based TRNG that offers both process-voltage-temperature tolerance and adjustable probability.

翻译：蒙特卡洛粒子输运问题在科学计算中扮演着重要角色，但在现有冯·诺依曼架构上求解此类问题时，随机分支与非规则内存访问导致计算效率低下，根本原因在于随机算法与确定性硬件之间存在本质失配。为弥合这一差距，我们提出MCPT-Solver——一种基于自旋器件的硬件真随机数生成器（TRNG），通过贝叶斯推理网络架构实现输出概率可调，专用于高效求解包括蒙特卡洛粒子输运问题在内的随机应用。首先，我们利用自旋器件的随机翻转特性为TRNG提供高质量熵源，并通过优化控制逻辑与写入机制设计，实现高生成吞吐量及工艺-电压-温度容限。其次，提出硬件贝叶斯推理网络以实现概率可调随机数输出。最后，构建系统级仿真框架评估MCPT-Solver。实验结果表明，MCPT-Solver在求解输运问题时均方误差达到7.6e-6，同时相对通用处理器展现出显著加速效果。此外，MCPT-Solver的吞吐量达185 Mb/s，面积为27.8 μm²/bit，能耗为8.6 pJ/bit，成为首个兼具工艺-电压-温度容限与概率可调特性的自旋基TRNG。