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Performance and Power Optimization for Cognitive Processor Design Using Deep-Submicron Very Large Scale Integration (VLSI) Technology
Final technical rept. Oct 2008-Oct 2009
STATE UNIV OF NEW YORK AT BINGHAMTON
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In the first part of this project, we investigated the performance and power optimization techniques of the floating point unit design as a part of the Air Force Research Laboratory, AFRL cognitive processor project. Our main focus was on exploring different design and synthesis methodologies that lead to the optimized area and power consumption, while fulfilling the performance requirements. Other tasks in this part included tight integration and interaction of logicphysical synthesis, custom circuit design, etc. Simulation and timing analysis results show that our post-layout designs met the area, timing and power requirements of the project. In the second part of the project, we developed a multi-layer cognitive model and algorithm for intelligent text recognition. The algorithm integrates three layers of different cognitive computing models in order to achieve the best accuracy in optical text recognition, as well as the best computation performance on a massively parallel computing cluster. In the first layer, we developed a novel neural network model that performs character recognition from images. The new model is able to provide more than one answer to the input image that is essential for the second layer, word-level recognition based on cogent confabulation. The word confabulation layer also provides multiple candidates that will be cross-checked by the third layer, the sentence confabulation algorithm. We believe that the multi-layer cognitive model concept invented by this project has significant innovation potential in the area of optical text recognition, machine learning and natural language processing.
APPROVED FOR PUBLIC RELEASE