# Mesorasi: Architecture Support for Point Cloud Analytics via Delayed-Aggregation

arXiv: https://arxiv.org/abs/2008.06967 · HTML: https://mlsanity.com/paper/2008.06967/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Aug 2020.

## Built on by (most cited first, 11 of 11)

- [TorchSparse++: Efficient Training and Inference Framework for Sparse Convolution on GPUs](https://mlsanity.com/paper/2311.12862/) (2023)
- [E2PNet: Event to Point Cloud Registration with Spatio-Temporal Representation Learning](https://mlsanity.com/paper/2311.18433/) (2023)
- [FlexGaussian: Flexible and Cost-Effective Training-Free Compression for 3D Gaussian Splatting](https://mlsanity.com/paper/2507.06671/) (2025)
- [DeepMix: Mobility-aware, Lightweight, and Hybrid 3D Object Detection for Headsets](https://mlsanity.com/paper/2201.08812/) (2022)
- [SPADE: Sparse Pillar-based 3D Object Detection Accelerator for Autonomous Driving](https://mlsanity.com/paper/2305.07522/) (2023)
- [Selectively Dilated Convolution for Accuracy-Preserving Sparse Pillar-based Embedded 3D Object Detection](https://mlsanity.com/paper/2408.13798/) (2024)
- [Towards Energy-Efficiency by Navigating the Trilemma of Energy, Latency, and Accuracy](https://mlsanity.com/paper/2409.04018/) (2024)
- [FastPoint: Accelerating 3D Point Cloud Model Inference via Sample Point Distance Prediction](https://mlsanity.com/paper/2507.23480/) (2025)
- [FractalCloud: A Fractal-Inspired Architecture for Efficient Large-Scale Point Cloud Processing](https://mlsanity.com/paper/2511.07665/) (2025)
- [Spira: Exploiting Voxel Data Structural Properties for Efficient Sparse Convolution in Point Cloud Networks](https://mlsanity.com/paper/2511.20834/) (2025)
- [FlashFPS: Efficient Farthest Point Sampling for Large-Scale Point Clouds via Pruning and Caching](https://mlsanity.com/paper/2604.17720/) (2026)

## Abstract (arXiv)

Point cloud analytics is poised to become a key workload on battery-powered embedded and mobile platforms in a wide range of emerging application domains, such as autonomous driving, robotics, and augmented reality, where efficiency is paramount. This paper proposes Mesorasi, an algorithm-architecture co-designed system that simultaneously improves the performance and energy efficiency of point cloud analytics while retaining its accuracy. Our extensive characterizations of state-of-the-art point cloud algorithms show that, while structurally reminiscent of convolutional neural networks (CNNs), point cloud algorithms exhibit inherent compute and memory inefficiencies due to the unique characteristics of point cloud data. We propose delayed-aggregation, a new algorithmic primitive for building efficient point cloud algorithms. Delayed-aggregation hides the performance bottlenecks and reduces the compute and memory redundancies by exploiting the approximately distributive property of key operations in point cloud algorithms. Delayed-aggregation let point cloud algorithms achieve 1.6x speedup and 51.1% energy reduction on a mobile GPU while retaining the accuracy (-0.9% loss to 1.2% gains). To maximize the algorithmic benefits, we propose minor extensions to contemporary CNN accelerators, which can be integrated into a mobile Systems-on-a-Chip (SoC) without modifying other SoC components. With additional hardware support, Mesorasi achieves up to 3.6x speedup.