Computing stands to be radically improved by neuromorphic computing (NMC) approaches inspired by the brain's incredible efficiency and capabilities. Most NMC research, which aims to replicate the brain's computational structure and architecture in man-made hardware, has focused on artificial intelligence; however, less explored is whether this brain-inspired hardware can provide value beyond cognitive tasks. We demonstrate that high-degree parallelism and configurability of spiking neuromorphic architectures makes them well-suited to implement random walks via discrete time Markov chains. Such random walks are useful in Monte Carlo methods, which represent a fundamental computational tool for solving a wide range of numerical computing tasks. Additionally, we show how the mathematical basis for a probabilistic solution involving a class of stochastic differential equations can leverage those simulations to provide solutions for a range of broadly applicable computational tasks. Despite being in an early development stage, we find that NMC platforms, at a sufficient scale, can drastically reduce the energy demands of high-performance computing (HPC) platforms.

中文翻译：

---

节能随机游走计算的神经形态缩放优势

受大脑令人难以置信的效率和能力启发的神经形态计算 (NMC) 方法将从根本上改进计算。大多数旨在在人造硬件中复制大脑计算结构和架构的 NMC 研究都集中在人工智能上。然而，很少有人探索这种受大脑启发的硬件是否可以提供超越认知任务的价值。我们证明了尖峰神经形态架构的高度并行性和可配置性使它们非常适合通过离散时间马尔可夫链实现随机游走。这种随机游走在蒙特卡罗方法中很有用，蒙特卡罗方法代表了解决各种数值计算任务的基本计算工具。此外，我们展示了涉及一类随机微分方程的概率解决方案的数学基础如何利用这些模拟为一系列广泛适用的计算任务提供解决方案。尽管处于早期开发阶段，我们发现 NMC 平台在足够的规模下可以大大降低高性能计算 (HPC) 平台的能源需求。