Algorithm and architecture for a multiple-field context-driven search engine using fully-parallel clustered associative memories

In this paper, a context-driven search engine is presented based on a new family of associative memories. It stores only the associations between items from multiple search fields in the form of binary links, and merges repeated field items to reduce the memory requirements. It achieves 13.6× reduction in memory bits and accesses, and 8.6× reduced number of clock cycles in search operation compared to a classical field-based search structure using content-addressable memory. Furthermore, using parallel computational nodes in the proposed search engine, it achieves five orders of magnitude reduced number of clock cycles compared to a CPU-based counterpart running a classical search algorithm in software.