PointODE: Lightweight Point Cloud Learning with Neural Ordinary Differential Equations on Edge
This work addresses the challenge of deploying point cloud learning on embedded edge devices, offering a practical solution for real-world applications with limited resources, though it is incremental in optimizing existing methods for efficiency.
The paper tackles the problem of running deep learning-based point cloud applications on resource-constrained edge devices by introducing PointODE, a lightweight architecture that uses Neural ODEs to compress parameters, achieving competitive accuracy with 0.58M parameters and a dedicated FPGA accelerator that speeds up feature extraction by 4.9x and improves energy-efficiency by 3.5x.
Embedded edge devices are often used as a computing platform to run real-world point cloud applications, but recent deep learning-based methods may not fit on such devices due to limited resources. In this paper, we aim to fill this gap by introducing PointODE, a parameter-efficient ResNet-like architecture for point cloud feature extraction based on a stack of MLP blocks with residual connections. We leverage Neural ODE (Ordinary Differential Equation), a continuous-depth version of ResNet originally developed for modeling the dynamics of continuous-time systems, to compress PointODE by reusing the same parameters across MLP blocks. The point-wise normalization is proposed for PointODE to handle the non-uniform distribution of feature points. We introduce PointODE-Elite as a lightweight version with 0.58M trainable parameters and design its dedicated accelerator for embedded FPGAs. The accelerator consists of a four-stage pipeline to parallelize the feature extraction for multiple points and stores the entire parameters on-chip to eliminate most of the off-chip data transfers. Compared to the ARM Cortex-A53 CPU, the accelerator implemented on a Xilinx ZCU104 board speeds up the feature extraction by 4.9x, leading to 3.7x faster inference and 3.5x better energy-efficiency. Despite the simple architecture, PointODE-Elite shows competitive accuracy to the state-of-the-art models on both synthetic and real-world classification datasets, greatly improving the trade-off between accuracy and inference cost.