In-Memory Search for Highly Efficient Image Retrieval

Finding similar images in real time plays a key role in information retrieval and serves as an indispensable function of the search engine. However, image retrieval involves massive distance computation. With the increase in image data volume and dimension, distance computation is suffering from huge power consumption and high computational complexity. Despite the remarkable advantages in energy efficiency shown by nonvolatile content addressable memory (nvCAM)-based in-memory search, achieving software-comparable search accuracy remains a critical challenge under the impact of device variations and other nonideal factors. Here, a heterogeneous image retrieval system combining highly parallel in-memory search with a high-precision digital system is reported. Hamming distance (HD) can be calculated in situ with a few memory read operations on the memristor-based CAM, and several similar images are fetched for further high-precision rerank in the digital system. This heterogeneous computing system shows high energy efficiency (50x) compared to the CPU and higher search accuracy than the fully in-memory computing method, thus alleviating the efficiency bottleneck of CPU-based image retrieval.