A highly-parallel match architecture for AI production systems using application-specific associative matching processors

Here, a highly-parallel two-layer match architecture using specific associative matching processors (AMPs) is proposed to speed up the execution time of match process of AI production systems. Each AMP comprises a 2D array of content-addressable memories, called CAM blocks. The architecture first compiles the left-hand (LHS) of each production into a symbolic form, and then assigns a number of contiguous CAM blocks in an AMP to the patterns in the LHS of each production individually. Those CAM blocks are used not only to buffer the database of current assessment (also called the working memory, WM), but also to support the functions of parallely evaluating interconditions among patterns of productions. The set of productions that are affected during a match cycle can be evaluated parallely and independently among their associating CAM blocks resided in the AMPs. Preliminary simulation result shows that the novel architecture provides the opportunity to at least ten-fold the performance of conventional forward-chaining production sytems