GPU-Accelerated Hypergraph Partitioning and Placement to Map SNNs on Neuromorphic Hardware

2026-09-07

AI Summary

研究提出了一种GPU加速的超图划分与放置方法，用于将SNN映射到神经形态硬件上，以减少通信流量并提高系统性能。

Abstract

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.

Problem

• SNNs
• neuromorphic hardware
• hypergraph partitioning
• placement
• communication traffic

Innovation

• GPU-accelerated pipeline
• hypergraph partitioning
• placement refinement
• neuromorphic hardware
• spike communication