用神经形态芯片高效求解稀疏有限元问题，精度可比传统方法。
Solving Sparse Finite Element Problems on Neuromorphic Hardware
- 将有限元的邻域交互映射为动态更新的神经群体，模拟物理规律。
- 在Poisson方程上达到与传统方法相当的精度和可扩展性。
- 适用于英特尔Loihi 2平台，适合复杂网格与动态系统建模。
我们证明了可扩展的神经形态硬件能够实现有限元方法，这是工程与科学发现中的关键数值方法。该方法将相邻有限元间的稀疏相互作用映射为小规模神经群体，这些群体根据目标问题的物理规律动态更新。针对描述引力场、静电场等众多物理系统的泊松方程，这种受大脑皮层启发的神经电路在保持与传统方法相当的数值精度和可扩展性的同时，实现了内在并行与能效优势。我们在英特尔Loihi 2平台上验证了该方法，并展示了其在非平凡网格几何与动态系统中的拓展能力。
原文摘要 · Abstract (English)
We demonstrate that scalable neuromorphic hardware can implement the finite element method, which is a critical numerical method for engineering and scientific discovery. Our approach maps the sparse interactions between neighboring finite elements to small populations of neurons that dynamically update according to the governing physics of a desired problem description. We show that for the Poisson equation, which describes many physical systems such as gravitational and electrostatic fields, this cortical-inspired neural circuit can achieve comparable levels of numerical accuracy and scaling while enabling the use of inherently parallel and energy-efficient neuromorphic hardware. We demonstrate that this approach can be used on the Intel Loihi 2 platform and illustrate how this approach can be extended to nontrivial mesh geometries and dynamics.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。