A Low-power and Real-time 3D Object Recognition Processor with Dense RGB-D Data Acquisition in Mobile Platforms

• Published in: 2022 IEEE Symposium in Low-Power and High-Speed Chips (COOL CHIPS) pp. 1 - 3
• Main Authors: Im, Dongseok, Park, Gwangtae, Ryu, Junha, Li, Zhiyong, Kang, Sanghoon, Han, Donghyeon, Lee, Jinsu, Park, Wonhoon, Kwon, Hankyul, Yoo, Hoi-Jun
• Format: Conference Proceeding
• Language: English
• Published: IEEE 20.04.2022
• Subjects: Computer architecture; Data acquisition; Energy consumption; Estimation; Neural networks; Point cloud compression; Three-dimensional displays
• DOI: 10.1109/COOLCHIPS54332.2022.9772667

A low-power and real-time 3D object recognition with RGBD data acquisition system-on-chip (SoC) is proposed. By synthesizing dense RGB-D data through monocular depth estimation, the proposed system reduces the sensor power for 3D data acquisition by ×27.3 lower. Moreover, the proposed processor reduces the energy consumption of a point cloud based neural network (PNN) exploiting bit-slice-level computation and a point feature reuse method with a pipelined architecture. Additionally, the processor supports the point sampling and grouping algorithms of the PNN with a unified point processing core. Finally, the processor achieves 210.0 mW while implementing 34.0 frame-per-second (fps) end-to-end RGB-D acquisition and 3D object recognition.