发表机构
DEIB, Politecnico di Milano（德伊布，米兰理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对神经形态硬件上的SNN映射，提出GPU加速的超图划分与布局流水线，通过多级划分和递归二分优化，显著降低延迟和能耗，并大幅提升映射速度。
AI 中文摘要
在神经形态硬件上运行的SNN利用脉冲在核心网格上实现稀疏且节能的通信。反过来，系统性能在很大程度上取决于神经元到核心的分配，即映射。由于硬件具有核心间多播和核心内脉冲复制的特性，我们将SNN建模为超图，以利用这两种机会减少通信流量。因此，映射包含两个NP难问题：超图划分和在核心晶格上的布局。这两个问题的高质量解决方案至关重要，但随着网络规模扩展到数百万个神经元，难度越来越大。因此，我们提出了一种GPU加速的SNN映射流水线：围绕硬件约束设计了一种多级划分方案，而布局通过递归二分初始化，随后通过重复交换将强连接的核心拉在一起的细化步骤。基于模型的实验表明，与现有的顺序工具相比，脉冲移动的延迟降低了16%以上，能耗降低了42%，而我们的并行映射器平均快18-280倍。
英文摘要
SNNs running on neuromorphic hardware use spikes to achieve sparse and energy-efficient communication over a mesh of cores. In turn, system performance heavily depends on the assignment of neurons to cores: the mapping. Since hardware features inter-core multicast and intra-core replication of spikes, we model SNNs as hypergraphs to exploit both opportunities for reducing communication traffic. Mapping thus comprises two NP-hard problems: hypergraph partitioning and placement on the lattice of cores. High-quality solutions to both are critical, yet increasingly difficult as networks scale to millions of neurons. Therefore, we propose a GPU-accelerated pipeline for SNN mapping: a multi-level partitioning scheme is devised around hardware constraints, while placement is initialized through recursive bisection, followed by refinement pulling together strongly connected cores through repeated swaps. Model-based experiments show upwards of 16% lower latency and 42% lower energy for spike movements over existing sequential tools, while our parallel mapper is on average 18-280x faster.
Comments6 pages
