Work-efficient parallel non-maximum suppression for embedded GPU architectures

With the emergence of GPU computing, deep neural networks have become a widely used technique for advancing research in the field of image and speech processing. In the context of object and event detection, sliding-window classifiers require to choose the best among all positively discriminated candidate windows. In this paper, we introduce the first GPU-based non-maximum suppression (NMS) algorithm for embedded GPU architectures. The obtained results show that the proposed parallel algorithm reduces the NMS latency by a wide margin when compared to CPUs, even clocking the GPU at 50% of its maximum frequency on an NVIDIA Tegra K1. In this paper, we show results for object detection in images. The proposed technique is directly applicable to speech segmentation tasks such as speaker diarization.