StereoEngine: An FPGA-Based Accelerator for Real-Time High-Quality Stereo Estimation With Binary Neural Network
Gang Chen, Yehua Ling, Tao He, Haitao Meng, Shengyu He, Yu Zhang, Kai Huang
Abstract
Stereo estimation is essential to many applications such as mobile autonomous robots, most of which ask for real-time response, high energy, and storage efficiency. Deep neural networks (DNNs) have shown to yield significant gains in improving accuracy. However, these DNN-based algorithms are challenging to be deployed on energy and resource-constrained devices due to the high computational complexities of DNNs. In this article, we present StereoEngine, a fully pipelined end-to-end stereo vision accelerator that computes accurate dense depth in a real-time and energy-efficient manner. An efficient stereo algorithm is developed and optimized for a high-quality hardware-friendly implementation, that leverages binary neural network (BNN) to learn discriminative binary descriptors to improve the disparity. The design of StereoEngine is a standalone DNN-based stereo vision system where all processing procedures are implemented on a hardware platform. The effectiveness of StereoEngine is evaluated by comprehensive experiments. Compared with software-based implementations on the highend and embedded Nvidia GPUs, StereoEngine achieves up to 3×, 13×, and 50× speedups, as well as up to 211×, 58×, and 73× energy efficiency improvement, respectively. Furthermore, StereoEngine achieves leading accuracy when compared to state-of-the-art hardware implementations on the challenging KITTI dataset.