Content-addressable memory (CAM) has been part of the memory market for more than five decades. CAM can carry out a single clock cycle lookup based on the content rather than an address. Thanks to this attractive feature, CAM is utilized in memory systems where a high-speed content lookup technique is required. However, typical CAM applications only support exact matching, as opposed to approximate matching, where a certain Hamming distance (several mismatching characters between a query pattern and the dataset stored in CAM) needs to be tolerated. Recent interest in approximate search has led to the development of new CAM-based alternatives, accelerating the processing of large data workloads in the realm of big data, genomics, and other data-intensive applications. In this review, we provide an overview of approximate CAM and describe its current and potential applications that would benefit from approximate search computing.
