MCPT-Solver: An Monte Carlo Algorithm Solver Using MTJ Devices for Particle Transport Problems
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arXiv
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| Autores principales: | , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866911552962035712 |
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| author | Fu, Siqing Wu, Lizhou Li, Tiejun Xie, Xuchao Zhang, Chunyuan Ma, Sheng Zhang, Jianmin Tang, Yuhan Tang, Jixuan |
| author_facet | Fu, Siqing Wu, Lizhou Li, Tiejun Xie, Xuchao Zhang, Chunyuan Ma, Sheng Zhang, Jianmin Tang, Yuhan Tang, Jixuan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_28042 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | MCPT-Solver: An Monte Carlo Algorithm Solver Using MTJ Devices for Particle Transport Problems Fu, Siqing Wu, Lizhou Li, Tiejun Xie, Xuchao Zhang, Chunyuan Ma, Sheng Zhang, Jianmin Tang, Yuhan Tang, Jixuan Hardware Architecture 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. |
| title | MCPT-Solver: An Monte Carlo Algorithm Solver Using MTJ Devices for Particle Transport Problems |
| topic | Hardware Architecture |
| url | https://arxiv.org/abs/2603.28042 |