  3D object detection is an important task, especially in the autonomous
driving application domain. However, it is challenging to support the real-time
performance with the limited computation and memory resources on edge-computing
devices in self-driving cars. To achieve this, we propose a compiler-aware
unified framework incorporating network enhancement and pruning search with the
reinforcement learning techniques, to enable real-time inference of 3D object
detection on the resource-limited edge-computing devices. Specifically, a
generator Recurrent Neural Network (RNN) is employed to provide the unified
scheme for both network enhancement and pruning search automatically, without
human expertise and assistance. And the evaluated performance of the unified
schemes can be fed back to train the generator RNN. The experimental results
demonstrate that the proposed framework firstly achieves real-time 3D object
detection on mobile devices (Samsung Galaxy S20 phone) with competitive
detection performance.

Computer Vision and Pattern Recognition;Artificial Intelligence