R. A. Cagle, R. B. Holl, and R. F. DeMara, "Multifunction Content Addressable Memory for Parallel Speech Understanding," in Proceedings of the 1994 IEEE Southcon Conference (Southcon94), pp. 320 - 325, Orlando, Florida, U.S.A., March 29 - 31, 1994. Abstract: Content Addressable Memories (CAMs) allow considerably finer-grained parallelism than conventional shared or distributed memory multi-processors. This fine-grained Processor-In-Memory concept can be employed to a large degree during Semantic Network processing in support of Artificial Intelligence (AI) with specific applications in speech and natural language processing. A special-purpose CAM configuration is presented based on requirements for a nominally-sized 64 K node semantic network with 8 bit-markers and 32 relationship types. Analysis for a target application shows that the extensive use of parallel Marker-Propagation and Set Theoretic Operations yields approximately 30-fold speedup over systems with standard Random Access Memories