Optimizing Memory Hierarchy in Machine Vision Computers
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This work will focus on an investigation and characterization of image          processing algorithms and their relationship to image representation            schemes.  Classic image processing functions manifest significant               differences in memory access patterns, i.e. spatial and temporal                locality, from the standard "general purpose" routines which have               historically been used as the metrics of optimal system design.  Image          processing alogrithms have not been characterized as a seperate class           of functions executing in a uniprocessor system.  It is postulated that         important increases in performance can be obtained by customization of          the computing architecture.  This enhanced performance will be obtained         without resorting to highly parallel systems and the resulting problems         of decreased mean time between failures and highly complex programming          issues.  Items to be studied are memory hierarchy and caching                   strategies as well as stored image representation schemes.  Using               stochastic models of behavior an optimal image processing architecture          and stored image representation scheme will be designed.
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