## 4 Experiment
### Main Results
#### 4.2.3 Ablation Study on the Pruning Ratio  
We also investigated the impact of pruning magnitude on model performance and acceleration ratio, as depicted in Figures 11 and 12. mIOU is normalized as the ratio to the unpruned version. Our analysis indicates that with an increment in pruning intensity, a discernible degradation in the mean Intersection over Union (mIOU) is observed. This decrement is primarily attributable to the shift in the categorical composition of the pruned points, which transitions progressively from background to foreground categories with increasing levels of pruning. In particular, substantial overlap exists in the background category within adjacent frames of point clouds. Consequently, most of the pruning points are categorized under the background when the pruning intensity ranges between 0.3 and 0.4 on nuScenes-LiDARSeg and 0.2 and 0.3 on SemanticKITTI. However, it is noteworthy that the foreground categories also exhibit a degree of overlap. As the pruning intensity further escalates, points associated with the foreground category are also excised, precipitating a pronounced decline in accuracy.  
\begin{table}
\begin{tabular}{l c c} \hline  & **nuScenes-LiDARSeg** & **SemanticKITTI** \\ \hline MinkUnet (SIMD) & 2.11 & 11.125 \\ \hline MinkUnet42 (prune+SIMD) & 1.89 & 9.175 \\ \hline SPVCNN (SIMD) & 2.96 & 14.59 \\ \hline SPVCNN (prune+SIMD) & 2.63 & 12.455 \\ \hline \end{tabular}
\end{table}
Table 6: Results of average latency (s) on SemanticKITTI and nuScenes-LiDARSeg val set. MinkUnet (SIMD) and SPVCNN (SIMD) denote the optimized Gather-GEMMs-Scatter dataflow.  
\begin{table}
\begin{tabular}{l c c c c c} \hline (channel\_in, channel\_out) & (4, 8) & (8, 16) & (16, 32) & (32, 64)) & (64, 128) & (128, 256) \\ \hline Time consumption / ms (original) & 90,140 & 95,476 & 102,622 & 107,569 & 110,473 & 106,146 \\ \hline Time consumption / ms (SIMD) & 559 & 899 & 1745 & 2794 & 5676 & 10,255 \\ \hline Speedup & 161.25 & 106.20 & 58.81 & 38.50 & 19.46 & 10.35 \\ \hline \end{tabular}
\end{table}
Table 4: Different layers’ time consumption and its speedup (channels ranging from 4 to 256).  
\begin{table}
\begin{tabular}{l c c c c c} \hline (channel\_in, channel\_out) & (256, 128) & (128, 64) & (64, 32) & (32, 16)) & (16, 10) \\ \hline Time consumption / ms (original) & 105,044 & 105,444 & 111,339 & 101,702 & 93,711 \\ \hline Time consumption / ms (SIMD) & 9189 & 4665 & 2587 & 1176 & 807 \\ \hline Speedup & 11.43 & 22.60 & 43.04 & 86.48 & 116.12 \\ \hline \end{tabular}
\end{table}
Table 5: Different layers’ time consumption and speedup (channels ranging from 256 to 10 with transposed).  
This phenomenon underscores the delicate balance required in pruning strategies, which aims to maximize computational efficiency while minimizing the adverse impact on model performance. Effective pruning algorithms must, therefore, account for the intricate composition of point cloud data, ensuring that foreground information critical to task performance is preserved as much as possible. This careful consideration is essential for achieving optimal trade-offs between acceleration and accuracy, thereby enhancing the practical applicability of pruning techniques in real-world scenarios.