## 1 Introduction
Sparse 3D convolution effectively leverages point cloud data's inherent sparsity, significantly reducing computational costs and making large-scale point cloud processing more feasible. Several libraries, such as MinkowskiEngine [16], Spconv [17], and Torchsparse [18; 19], provide efficient implementations of sparse 3D convolution. These libraries utilize dataflows similar to Gather-GEMMs-Scatter operations to handle irregular computations in point clouds. Although the sparsity of point clouds can be efficiently leveraged, the transformation involved in Gather and Scatter operations remains exceedingly time-consuming when converting between the storage of discrete data and continuous data streams suitable for modern computational processing. Both Gather and Scatter operations involve tremendous data rearrangement, so it takes considerable time to transform irregular data into a regular format suitable for matrix multiplication. This is especially true for computing devices like CPUs because the methods used for effectively performing computations on these platforms present a significant challenge. This urgency compels us to adopt suitable methods to minimize resource expenditure on such tasks, thereby enhancing processing speed. Taking Intel's or RISC-V's series of CPUs as an example, they provide vector instruction sets that enable developers to more effectively utilize hardware resources for achieving high-performance computations. To address these challenges, this work first proposes a pruning technique that uses the temporal correlation between adjacent frames to alleviate redundant calculations and speed up inference. The point cloud is stored in the tuple form and captured at a rapid pace by LiDAR. That means moving things can show changes in coordinates, while unmoving things are the opposite. Inspired by this, we prune the 3D point cloud by the temporal correlation between two adjacent frames to achieve speedup in scenarios like autonomous driving. Furthermore, given that autonomous vehicles must navigate safely at high speeds while maintaining reasonable energy consumption to support adequate computational performance, deploying high-power GPUs on autonomous vehicles is not feasible. Consequently, we leverage power-efficient computing platforms like CPUs for accelerated inference. To further speed up, we analyze the runtime bottleneck caused by the conflict between discrete storage and continuous computation in the Gather-GEMMs-Scatter data flow. We optimize the Gather and Scatter operations using SIMD techniques to alleviate these time-consuming operations. We compare it with the state-of-the-art sparse inference engine TorchSparse 2.0 to show our proposed method can achieve 1.2\(\times\) speedup for MinkUnet and SPVCNN without a significant accuracy loss. In particular, our SIMD-based data movement can achieve more than 5\(\times\) speedup. In summary, the contributions of this paper can be delineated as follows:  
* We propose a pruning technique that exploits the temporal correlations between consecutive frames to alleviate redundant computations and accelerate inference. * We optimize data movement by employing Single Instruction Multiple Data (SIMD) techniques to enhance the efficiency of the time-intensive Gather and Scatter operations within the dataflow.