  Object detection plays an important role in self-driving cars for security
development. However, mobile systems on self-driving cars with limited
computation resources lead to difficulties for object detection. To facilitate
this, we propose a compiler-aware neural pruning search framework to achieve
high-speed inference on autonomous vehicles for 2D and 3D object detection. The
framework automatically searches the pruning scheme and rate for each layer to
find a best-suited pruning for optimizing detection accuracy and speed
performance under compiler optimization. Our experiments demonstrate that for
the first time, the proposed method achieves (close-to) real-time, 55ms and
99ms inference times for YOLOv4 based 2D object detection and PointPillars
based 3D detection, respectively, on an off-the-shelf mobile phone with minor
(or no) accuracy loss.
