多线程内核让神经形态计算在边缘设备高效运行。
A Multi-Threading Kernel for Enabling Neuromorphic Edge Applications
- 设计多线程内核，实现神经形态应用在边缘设备的并行处理。
- 在中等规模SNN上提速4倍，在同步发放网络上提速1.7倍。
- 自动负载均衡多核资源，能效比静态分配高70%，适合移动设备。
脉冲神经网络（SNN）具有稀疏、事件驱动的特性，适合神经形态计算应用。本文提出一种多线程内核，使神经形态应用可在边缘设备上直接处理传感输入，无需依赖云端服务。该内核在中等规模SNN上相比单线程处理速度提升4倍，在同步发放网络（Synfire network）上提升1.7倍。同时，它能自动平衡多核处理器（如当前移动设备中的ARM架构）上的所有核心负载，相比静态核心分配，能效最高提升70%。该工作有助于开发低尺寸、重量和功耗（SWaP）的边缘应用，并支持神经形态芯片的原型集成。
原文摘要 · Abstract (English)
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.
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。