MCPT-Solver: An Monte Carlo Algorithm Solver Using MTJ Devices for Particle Transport Problems
This addresses computing bottlenecks for scientific applications like particle transport by offering a specialized hardware solution, though it appears incremental as it builds on existing spin device technology.
The paper tackles the inefficiency of solving Monte Carlo particle transport problems on von Neumann architectures by proposing MCPT-Solver, a spin-based hardware true random number generator with tunable probability, which achieves a mean squared error of 7.6e-6 and throughput of 185 Mb/s with low area and energy consumption.
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.