An Energy-Efficient Block-Based Nonmaximum Suppression Engine for High-Parallel Postprocessing of Visual Object Detection

Nowadays, visual object detection (VOD) is widely used in many AI applications, such as autonomous driving, intelligent robotics, and smart surveillance. As an essential postprocessing step in VOD, nonmaximum suppression (NMS) is employed to generate bounding boxes as detection results. However, NMS is difficult to parallelize and computationally intensive, resulting in high processing latency and energy consumption. To address this issue, this brief proposes an energy-efficient block-based NMS engine that incorporates both algorithm- and hardware-level design techniques to improve processing speed and energy efficiency. These techniques include a block-NMS scheme, an adaptive hybrid sorting architecture (AHSA), and a reconfigurable pipeline-based block-NMS computation architecture. The proposed engine is implemented in 28-nm CMOS technology. Compared with the state-of-the-art designs, it achieves the highest performance (237.97 GOPS) and energy efficiency (8.71 TOPS/W), while delivering results fully equivalent to those of the original NMS.