Spiking Neural Networks (SNNs) have sparse, event driven processing that can leverage neuromorphic applications. In this work, we introduce a multi-threading kernel that enables neuromorphic applications running at the edge, meaning they process sensory input directly and without any up-link to or dependency on a cloud service. The kernel shows speed-up gains over single thread processing by a factor of four on moderately sized SNNs and 1.7X on a Synfire network. Furthermore, it load-balances all cores available on multi-core processors, such as ARM, which run today's mobile devices and is up to 70% more energy efficient compared to statical core assignment. The present work can enable the development of edge applications that have low Size, Weight, and Power (SWaP), and can prototype the integration of neuromorphic chips.

翻译：脉冲神经网络（SNNs）具有稀疏、事件驱动的处理特性，能够有效利用神经形态应用。本研究提出了一种多线程内核，使得神经形态应用能够在边缘端运行，即直接处理传感器输入，无需依赖或上传至云端服务。该内核在中等规模的SNN上相比单线程处理实现了四倍的加速增益，在Synfire网络上实现了1.7倍的加速。此外，它能够在多核处理器（如当前移动设备采用的ARM架构）上实现所有可用核心的负载均衡，与静态核心分配相比，能效提升高达70%。本工作有助于开发具有低尺寸、重量和功耗（SWaP）特性的边缘应用，并为神经形态芯片的集成提供原型验证。