@inproceedings{c_cagle_holl_southcon_94,
	abstract = {Content Addressable Memories (CAMs) allow considerably finer-grained parallelism than conventional shared or distributed memory multi-processors. This fine-grained {\&}ldquo;Processor-In-Memory{\&}rdquo; 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},
	address = {Orlando, Florida, U.S.A.},
	author = {Cagle, RA   and Holl, RB   and DeMara, RF  },
	citeulike-article-id = {87087},
	journal = {Southcon/94. Conference Record},
	keywords = {demara},
	pages = {320--325},
	title = {Multifunction content addressable memory for parallel speech understanding},
	url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arNumber=498125},
	year = {1994}
}