Spike sparsity is widely believed to enable efficient spiking neural network (SNN) inference on GPU hardware. We demonstrate this is an illusion: five distinct sparse computation strategies on Apple M3 Max all fail to outperform dense convolution, because SIMD architectures cannot exploit the fine-grained, unstructured sparsity of i.i.d. binary spikes. Instead, we propose Temporal Aggregated Convolution (TAC), which exploits convolution linearity to pre-aggregate $K$ spike frames before a single convolution call, reducing $T$ calls to $T/K$. On rate-coded data, TAC achieves 13.8times speedup with +1.6% accuracy on MNIST and +5.4% on Fashion-MNIST -- a simultaneous improvement in both speed and accuracy. However, on event-based data where the temporal dimension carries genuine motion information, TAC's temporal collapse is harmful. We therefore introduce TAC-TP (Temporal Preservation), which shares each group's convolution output across K independent LIF steps, preserving full temporal resolution for downstream layers. On DVS128-Gesture, TAC-TP achieves 95.1% accuracy (vs. 96.3% baseline) with 50% fewer convolution calls, while standard TAC drops to 91.3%. Our key finding is that the optimal temporal aggregation strategy is data-dependent: collapse the temporal dimension for rate-coded data (noise reduction) but preserve it for event data (information retention). Speedup is hardware-agnostic: TAC achieves 11.0times on NVIDIA V100, confirming the mechanism transfers across GPU architectures. All operators in the mlx-snn library are open source.

翻译：脉冲稀疏性被广泛认为能够在GPU硬件上实现高效的脉冲神经网络推理。我们证明这是一种错觉：在Apple M3 Max上测试的五种不同稀疏计算策略均未能超越密集卷积，因为SIMD架构无法有效利用独立同分布二元脉冲的细粒度、非结构化稀疏性。为此，我们提出时序聚合卷积，该方法利用卷积的线性特性，将$K$个脉冲帧预先聚合后执行单次卷积调用，从而将$T$次调用减少至$T/K$次。在速率编码数据上，TAC在MNIST数据集上实现了13.8倍加速且准确率提升1.6%，在Fashion-MNIST数据集上准确率提升5.4%——实现了速度与精度的同步提升。然而，对于时间维度承载真实运动信息的事件型数据，TAC的时间维度坍缩会产生负面影响。因此我们提出TAC-TP，该方法将每个卷积组的输出共享给K个独立的LIF步骤，为下游层保留完整的时间分辨率。在DVS128-Gesture数据集上，TAC-TP以减少50%卷积调用的代价实现了95.1%的准确率，而标准TAC准确率降至91.3%。我们的核心发现是：最优时序聚合策略具有数据依赖性——对速率编码数据应坍缩时间维度，而对事件数据则应保留时间维度。这种加速机制具有硬件无关性：TAC在NVIDIA V100上实现了11.0倍加速，证实了该机制在不同GPU架构间的可迁移性。mlx-snn库中的所有算子均已开源。