VLSI design of a power-efficient object detector using PCANet

Abstract

This paper presents the hardware architecture and VLSI implementations of a PCANet-based object detector. The proposed PCANet model, cascaded with a linear support vector machine, can achieve better classification performance than traditional handcrafted computer vision methods, yet it is significantly more power efficient than multi-layer convolutional neural networks. The proposed pipeline hardware architecture, when implemented using Synopsys 32nm process technology, results in 27.4fps while processing 1080P, with only 0.5watt power consumption. Targeted for the application of advanced driver assistance system, the proposed design is evaluated on road marking and traffic light dataset with an accuracy result of 96.8% and 93.1% respectively. Therefore, the proposed VLSI implementation of PCANet algorithm provides a high-throughput and power-efficient solution for object detection applications.