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Feature matching is an indispensable process for object recognition, which is an important issue for wearable devices with video analysis functionalities. To implement a low-power SoC for object recognition, the proposed visual vocabulary processor (VVP) is employed to accelerate the speed of feature matching. The VVP can transform hundreds of 128-D SIFT vectors into a 64-D histogram for object matching by using the binary-tree-based architecture, and 16 calculators for the computations of the Euclidean distances are designed for each of the two processors in each level. A total of 126 visual words can be saved in the six-level hierarchical memory, which instantly offers the data required for the matching process, and more than 5 times of bandwidth can be saved compared with the non-binary-tree-based architecture. As a part of the recognition SoC, the VVP is implemented with the 65-nm CMOS technology, and the experimental results show that the gate count and the average power consumption are 280 K and 5.6 mW, respectively.