Point cloud processing using non-volatile memories with circuit and sensor noise

We demonstrate the simulation of noise- dependent point cloud processing using a compact model of non-volatile memories (NVMs). We investigate how classification accuracy is affected by programming variations in NVMs, representing circuit noise, and distortions in point clouds, representing sensor noise. We employ a PointNet-based framework and explore how the inherent weight-sharing properties of PointNet can leverage NVM crossbars for energy-efficient processing. By benchmarking the performance of both NVM-based PointNet across various noise levels, we demonstrate the impact of different noise types on classification accuracy. Our findings show that while certain circuit and sensor noise degrade classification performance, our NVM-based PointNet achieves competitive results with reduced trainable parameters, providing a path toward neuromorphic 3D vision and computing with co-optimization of software and hardware. Our work highlights the potential for using NVM crossbars to efficiently handle noise-dependent processing in edge inference units.