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arXiv · 2605.23796

UniSpike: Accelerating Spiking Neural Networks on Neuromorphic Systems via Eliminating Address Redundancy

Qinghui Xing·Zhuo Chen·Xin Du·Ouwen Jin·Ming Zhang·Pan Lv·Ying Li·Shuiguang Deng·Gang Pan
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Abstract

Many-core neuromorphic systems accelerate Spiking Neural Networks (SNNs), yet their packet-based spike communication can spend substantial traffic and energy repeatedly transmitting destination addresses. This overhead is amplified by the small payload of spike packets: in representative workloads, duplicate address transmissions account for up to 49% of the total traffic. This paper presents UniSpike, a hardware-software co-design that removes address redundancy by aggregating spikes destined for the same core into compact packets. UniSpike combines destination-centric spike scheduling, lightweight runtime packet assembly hardware, and destination-aware SNN partitioning. Across diverse SNN workloads, UniSpike reduces traffic by 1.93$\times$ on average, delivering 1.77$\times$ speedup and 1.50$\times$ energy efficiency improvement over state-of-the-art designs.

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Qinghui Xing, Zhuo Chen, Xin Du, Ouwen Jin, Ming Zhang, Pan Lv, Ying Li, Shuiguang Deng, Gang Pan. 2026-05-22. UniSpike: Accelerating Spiking Neural Networks on Neuromorphic Systems via Eliminating Address Redundancy. https://arxiv.org/abs/2605.23796

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