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.

翻译：众核神经形态系统加速了脉冲神经网络（SNNs），但基于数据包的脉冲通信会因重复传输目标地址而消耗大量流量和能量。这一开销因脉冲数据包的有效载荷较小而被放大：在代表性工作负载中，重复的地址传输占总流量的比例高达49%。本文提出UniSpike，一种通过将发往同一核心的脉冲聚合为紧凑数据包来消除地址冗余的软硬件协同设计方案。UniSpike结合了以目标为中心的脉冲调度、轻量级运行时数据包组装硬件以及目标感知的SNN分区。在多种SNN工作负载下，UniSpike平均减少1.93倍的流量，相较于现有最优设计实现了1.77倍的加速和1.50倍的能效提升。