| Investigating Architectural Issues in Neuromorphic Computing |
Jun 2009 |
96 pages |
| Authors:
Richard W Linderman; Daniel Burns; Michael Moore; Qing Wu; Qinru Qiu; Tarek Taha; AIR FORCE RESEARCH LAB ROME NY INFORMATION DIRECTORATE
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 | This effort has explored the issues associated with the efficient mapping of neuromorphic computing strategies onto advanced computational architectures. This multidisciplinary effort combined concepts and research from diverse fields including computer architecture, neuroscience, cognitive psychology, cognitive modeling, dynamical systems, software and computer engineering. It explored multiple columnar cortical models reported in the literature, and produced new models by combining ideas with insights developed by the research team. These models range ... |
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| Performance Optimization for Pattern Recognition Using Associative Neural Memory |
Jun-2008 |
15 pages |
| Authors:
Qing Wu; Prakash Mukre; Richard Linderman; Tom Renz; Daniel Burns; Michael Moore; Qinru Qiu; AIR FORCE RESEARCH LAB ROME NY INFORMATION DIRECTORATE
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| 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 ... |
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| Accelerating Cogent Confabulation: An Exploration in the Architecture Design Space |
Jun-2008 |
10 pages |
| Authors:
Qinru Qiu; Daniel Burns; Michael Moore; Richard Linderman; Thomas Renz; Qing Wu; AIR FORCE RESEARCH LAB ROME NY INFORMATION DIRECTORATE
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| Cogent confabulation is a computation model that mimics the Hebbian learning, information storage, inter-relation of symbolic concepts, and the recall operations of the brain. The model has been applied to cognitive processing of language, audio and visual signals. This project focuses on how to accelerate the computation underlie confabulation based sentence completion through software and hardware optimization. Software implementation with appropriate data structures can improve the performance of the software ... |
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