论文标题
使用峰值网络和神经形态硬件解决稳态PDE
Solving a steady-state PDE using spiking networks and neuromorphic hardware
论文作者
论文摘要
神经形态处理器的广泛平行，尖峰的神经网络可以实现计算功能强大的公式。尽管最近的兴趣主要集中在机器学习任务上，但适当的应用程序的空间却很广，并且不断扩大。在这里，我们利用平行和事件驱动的结构使用随机行走方法来解决稳态热方程。随机步行可以使用随机神经元行为在尖峰神经网络中充分执行，我们提供了IBM Truenorth和Intel Loihi实现的结果。此外，我们将该算法定位为神经形态系统的潜在可扩展基准。
The widely parallel, spiking neural networks of neuromorphic processors can enable computationally powerful formulations. While recent interest has focused on primarily machine learning tasks, the space of appropriate applications is wide and continually expanding. Here, we leverage the parallel and event-driven structure to solve a steady state heat equation using a random walk method. The random walk can be executed fully within a spiking neural network using stochastic neuron behavior, and we provide results from both IBM TrueNorth and Intel Loihi implementations. Additionally, we position this algorithm as a potential scalable benchmark for neuromorphic systems.
