PRADA: Point Cloud Recognition Acceleration via Dynamic Approximation

Recent point cloud recognition (PCR) tasks tend to utilize deep neural network (DNN) for better accuracy. Still, the computational intensity of DNN makes them far from real-time processing, given the fast-increasing number of points that need to be processed. Based on the observation of the points tend to an uneven distribution that exposes strong clustering possibility and local pairs’ similarities, this paper proposes PRADA, an algorithm-architecture co-design that can accelerate PCR while reserving its accuracy. We propose dynamic approximation, which can approximate and eliminate the similar local pairs’ computations and recover their results by copying key local pairs’ features for PCR speedup without losing accuracy. For performance good, we then propose a PRADA architecture that can be built on any conventional DNN accelerator to dynamically approximate the similarity and skip the redundant DNN computation with memory accesses at the same time.