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Proceedings Paper

New generation of content addressable memories for associative processing
Author(s): H. Garton Lewis; Paul Giambalov
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Paper Abstract

Content addressable memories (CAMS) store both key and association data. A key is presented to the CAN when it is searched and all of the addresses are scanned in parallel to find the address referenced by the key. When a match occurs, the corresponding association is returned. With the explosion of telecommunications packet switching protocols, large data base servers, routers and search engines a new generation of dense sub-micron high throughput CAMS has been developed. The introduction of this paper presents a brief history and tutorial on CAMS, their many uses and advantages, and describes the architecture and functionality of several of MUSIC Semiconductors CAM devices. In subsequent sections of the paper we address using Associative Processing to accommodate the continued increase in sensor resolution, number of spectral bands, required coverage, the desire to implement real-time target cueing, and the data flow and image processing required for optimum performance of reconnaissance and surveillance Unmanned Aerial Vehicles (UAVs). To be competitive the system designer must provide the most computational power, per watt, per dollar, per cubic inch, within the boundaries of cost effective UAV environmental control systems. To address these problems we demonstrate leveraging DARPA and DoD funded Commercial Off-the-Shelf technology to integrate CAM based Associative Processing into a real-time heterogenous multiprocessing system for UAVs and other platforms with limited weight, volume and power budgets.

Paper Details

Date Published: 5 May 2000
PDF: 9 pages
Proc. SPIE 3905, 28th AIPR Workshop: 3D Visualization for Data Exploration and Decision Making, (5 May 2000); doi: 10.1117/12.384870
Show Author Affiliations
H. Garton Lewis, AIPS (United States)
Paul Giambalov, Music Semiconductors (United States)


Published in SPIE Proceedings Vol. 3905:
28th AIPR Workshop: 3D Visualization for Data Exploration and Decision Making
William R. Oliver, Editor(s)

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