  With the rapid development of GPU (Graphics Processing Unit) technologies and
neural networks, we can explore more appropriate data structures and
algorithms. Recent progress shows that neural networks can partly replace
traditional data structures. In this paper, we proposed a novel DNN (Deep
Neural Network)-based learned locality-sensitive hashing, called LLSH, to
efficiently and flexibly map high-dimensional data to low-dimensional space.
LLSH replaces the traditional LSH (Locality-sensitive Hashing) function
families with parallel multi-layer neural networks, which reduces the time and
memory consumption and guarantees query accuracy simultaneously. The proposed
LLSH demonstrate the feasibility of replacing the hash index with
learning-based neural networks and open a new door for developers to design and
configure data organization more accurately to improve information-searching
performance. Extensive experiments on different types of datasets show the
superiority of the proposed method in query accuracy, time consumption, and
memory usage.
