标题： NeuroPDE：基于自旋电子和铁电器件的类脑PDE求解器 显示英文标题

标题： NeuroPDE: A Neuromorphic PDE Solver Based on Spintronic and Ferroelectric Devices

摘要： 近年来，用于求解偏微分方程（PDEs）的新方法，如蒙特卡洛随机游走方法，引起了广泛关注。然而，由于传统冯·诺依曼架构缺乏内在的随机性，PDE求解器的性能受到限制。在本文中，我们介绍了NeuroPDE，这是一种用于神经形态PDE求解器的硬件设计，利用了新兴的自旋电子和铁电器件。NeuroPDE结合了能够进行概率传输的自旋神经元，以模拟随机游走，以及能够非易失性存储连续权重的铁电突触。与解析解相比，所提出的NeuroPDE在求解扩散方程时方差小于1e-2，展示了执行时间上3.48倍至315倍的加速优势，以及在能耗上比先进的基于CMOS的神经形态芯片2.7倍至29.8倍的优势。通过利用新兴器件固有的物理随机性，这项研究为未来的概率神经形态计算系统铺平了道路。 显示英文摘要

摘要： In recent years, new methods for solving partial differential equations (PDEs) such as Monte Carlo random walk methods have gained considerable attention. However, due to the lack of hardware-intrinsic randomness in the conventional von Neumann architecture, the performance of PDE solvers is limited. In this paper, we introduce NeuroPDE, a hardware design for neuromorphic PDE solvers that utilizes emerging spintronic and ferroelectric devices. NeuroPDE incorporates spin neurons that are capable of probabilistic transmission to emulate random walks, along with ferroelectric synapses that store continuous weights non-volatilely. The proposed NeuroPDE achieves a variance of less than 1e-2 compared to analytical solutions when solving diffusion equations, demonstrating a performance advantage of 3.48x to 315x speedup in execution time and an energy consumption advantage of 2.7x to 29.8x over advanced CMOS-based neuromorphic chips. By leveraging the inherent physical stochasticity of emerging devices, this study paves the way for future probabilistic neuromorphic computing systems.