The rapid advancement of intelligent systems, especially robotics and autonomous driving, is highly reliant on low-complexity and high-accuracy stereo matching algorithms. However, the performance of state-of-the-art stereo matching algorithms still has great space for improvement by gaining awareness of the implicit information hidden in the cost volume layers. In this paper, we propose a low-complexity local stereo matching algorithm named Cross-Layer Information Fusion (CLIF), to improve the matching accuracy by exploring the hidden information. First, we analyze and extract the hidden information into an auxiliary extractor using a novel fusion method. Second, we propose an information sharing strategy that transforms the extractor into a regularization term on each cost volume layer. Then we improve the design by re-constructing the information extractor between the adjacent cost volume layers and form a pipelined hardware architecture on the FPGA platform. Experimental results show that the proposed CLIF algorithm improves 6.53% average accuracy incurring negligible resources and performance impacts, compared to the state-of-the-art solutions.