Towards Real-Time 3D Object Detection with Pruning Search on Edge Devices (Pu Zhao, Northeastern University)

In autonomous driving, 3D object detection is essential as it provides basic knowledge about the environment. However, as deep learning based 3D detection methods are usually computation intensive, it is challenging to support real-time 3D detection on edge-computing devices with limited computation and memory resources. To facilitate this, we propose a compiler-aware pruning search framework, to achieve real-time inference of 3D object detection on the resource-limited mobile devices. Specifically, a generator is applied to sample better pruning proposals in the search space, and an evaluator is adopted to evaluate the sampled pruning proposal performance with Bayesian optimization. We demonstrate that the pruning search framework can achieve real-time 3D object detection on mobile (Samsung Galaxy S20 phone) with state-of-the-art detection performance.