A Feature Reuse Engine (FReE) is proposed to achieve low-power object recognition in video frames. Previous object recognition processors perform redundant processing for repeated features of sequential frames in video. Unlike previous works, proposed FReE reuses 58% of features from previous frame with inter-frame similarity. However, false reuse can degrade the object recognition accuracy. By pixel intensity-based Near Pixel Information (NPI), FReE can decide the reusability with 96% of accuracy. Additionally, to minimize latency and power overhead of FReE, a dedicated integral image generator and NPI generator are proposed. As a result, power consumption of object recognition processor is reduced by 31% with the proposed FReE which consumes only 1.92mW in a 130nm CMOS technology. The major power for object recognition is consumed in feature extraction from each video frames. In the case of HD 720p resolution, about 3,000 features are extracted from each video frames. To resolve the bottleneck, we propose feature reuse model which is based on inter-frame similarity. It reuses features of previous frame, while other previous object recognition processors processed repeatedly the redundant features of sequential frames in video. By this model, 58% of features can be reused from previous frame. However, in this approach, false reuse can degrade the object recognition accuracy. To achieve accurate feature reuse, we propose pixel- intensity-based Near Pixel Information (NPI) which considers the scale of the keypoints. Also, an integral image is adopted to reduce the NPI generation time. In addition, a dedicated hardware design of integral image generator and NPI generator minimize both latency and power overhead of the proposed Feature Reuse Engine (FReE). The remaining of this paper is organized as follows. Section II introduces the proposed feature reuse model. And Section III describes the architecture of the proposed FReE. Then implementation results will be covered at Section IV. Finally, Section V concludes this paper.
['Dongjoo Shin', 'Injoon Hong', 'Hoi-Jun Yoo']
An 1.92mW Feature Reuse Engine based on inter-frame similarity for low-power object recognition in video frames