Performance Optimization for Pattern Recognition Using Associative Neural Memory

Abstract

This paper describes the performance optimization in software and hardware solutions for a cognitive computing model called Brain State in a Box (BSB). This BSB model is implemented using two different configurations of the proposed architecture. The first implementation is a software only approach using the Cell Broadband Engine. The other implementation is a hybrid configurable computing platform which uses Field Programmable Gate Array (FPGA) for implementing the computation. To compensate its efficiency, the BSB based associative neural memory is applied for symbol and character recognition.

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Document Details

Document Type
Technical Report
Publication Date
Jun 01, 2008
Accession Number
ADA502126

Entities

People

  • Daniel Burns
  • Michael J Moore
  • Prakash Mukre
  • Qing Wu
  • Qinru Qiu
  • Richard Linderman
  • Tom Renz

Organizations

  • Air Force Research Laboratory

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Algorithms
  • Bandwidth
  • Broadband
  • C Programming Language
  • Computations
  • Computer Programming
  • Computer Programs
  • Computers
  • Computing System Architectures
  • Field Programmable Gate Arrays
  • Frequency
  • Instruction Set Architecture
  • Mathematical Models
  • Networks
  • Operating Systems
  • Pattern Recognition
  • Recognition

Fields of Study

  • Computer science

Readers

  • Brain and Cognitive Science; Experimental Psychology; Cognitive Neuroscience
  • Computational Modeling and Simulation
  • Computer Programming and Software Development.

Technology Areas

  • AI & ML