PointAcc: Efficient Point Cloud Accelerator (2110.07600v1)

Abstract: Deep learning on point clouds plays a vital role in a wide range of applications such as autonomous driving and AR/VR. These applications interact with people in real-time on edge devices and thus require low latency and low energy. Compared to projecting the point cloud to 2D space, directly processing the 3D point cloud yields higher accuracy and lower #MACs. However, the extremely sparse nature of point cloud poses challenges to hardware acceleration. For example, we need to explicitly determine the nonzero outputs and search for the nonzero neighbors (mapping operation), which is unsupported in existing accelerators. Furthermore, explicit gather and scatter of sparse features are required, resulting in large data movement overhead. In this paper, we comprehensively analyze the performance bottleneck of modern point cloud networks on CPU/GPU/TPU. To address the challenges, we then present PointAcc, a novel point cloud deep learning accelerator. PointAcc maps diverse mapping operations onto one versatile ranking-based kernel, streams the sparse computation with configurable caching, and temporally fuses consecutive dense layers to reduce the memory footprint. Evaluated on 8 point cloud models across 4 applications, PointAcc achieves 3.7X speedup and 22X energy savings over RTX 2080Ti GPU. Co-designed with light-weight neural networks, PointAcc rivals the prior accelerator Mesorasi by 100X speedup with 9.1% higher accuracy running segmentation on the S3DIS dataset. PointAcc paves the way for efficient point cloud recognition.

• The paper presents a novel accelerator, PointAcc, that leverages ranking-based kernels to reduce computational overhead in deep learning on sparse point clouds.
• The methodology overcomes inefficiencies from explicit nonzero neighbor searches and excessive data movement, boosting latency and energy efficiency.
• Evaluations demonstrate a 3.7-fold speedup over conventional GPUs and a 100-fold improvement over prior accelerators, with enhanced segmentation accuracy on the S3DIS dataset.

Insightful Overview of "PointAcc: Efficient Point Cloud Accelerator"

The paper "PointAcc: Efficient Point Cloud Accelerator" presents a novel approach for accelerating deep learning computations on point clouds, aiming to address the challenges posed by their inherently high sparsity and associated computational overhead. Deep learning on point clouds is crucial in applications such as autonomous driving and augmented/virtual reality, where low latency and energy efficiency are paramount. Traditional processing methods incur significant overhead due to the need for explicit determination of nonzero outputs and nonzero neighbor searches, which exacerbates data movement and computational inefficiency.

Key Contributions

1. Analysis of Modern Point Cloud Networks: The paper first establishes the performance bottlenecks associated with contemporary point cloud networks running on CPUs, GPUs, and TPUs. The authors identify that the mapping operations required for point cloud processing are unsupported by existing accelerators and cause substantial data movement and computational overhead.
2. Introduction of PointAcc: To address these challenges, the authors introduce PointAcc, a specialized accelerator for deep learning on point clouds. The accelerator design revolves around a versatile ranking-based kernel that efficiently manages operations such as mapping, streaming sparse computations, and fusing dense layers to lower memory demands.
3. Evaluations and Results: PointAcc was evaluated across multiple models and datasets, demonstrating a 3.7-fold speedup and 22-fold energy savings over a conventional RTX 2080Ti GPU setup. Against a prior state-of-the-art accelerator, Mesorasi, PointAcc provides a 100-fold speedup and significantly improves accuracy when performing segmentation on the S3DIS dataset.

Strong Numerical Results and Claims

The paper is notable for its strong empirical results. Testing PointAcc across eight point cloud models within four application domains, the accelerator outperformed traditional methods and specialized counterparts significantly. Critical numerical results presented included a 100-fold speedup against Mesorasi and substantial energy savings, emphasizing the efficiency of its ranking-based approach and memory management strategies. Such robust results underscore PointAcc's potential as a practical solution for real-time point cloud applications.

Implications and Future Developments

The significance of this research lies in its potential to make real-time point cloud analysis more feasible on edge devices, which is paramount for applications in autonomous vehicles and smart devices. By efficiently harnessing the sparsity of point clouds, PointAcc directly contributes to reducing latency and energy consumption in applied scenarios.

Theoretically, the adoption of a ranking-based kernel could inspire the redesign of accelerators in other domains where high-dimensional sparse data is prevalent. The focus on optimizing data movement, through mechanisms like temporal layer fusion and configurable caching, might also influence future architectures aiming for energy-efficient computing.

Looking ahead, further developments could explore adapting PointAcc's methodologies to a wider variety of sparse data structures beyond point clouds. Moreover, enhancements that combine this approach with evolving neural architectures would expand its applicability and operational efficiency.

In conclusion, the paper "PointAcc: Efficient Point Cloud Accelerator" offers significant contributions to hardware acceleration for point cloud data analysis, marked by both theoretical insights and substantial numerical validations. The innovative approaches in addressing sparsity and data movement challenges set a promising trajectory for future work in efficient AI hardware designs.