A Holistic Approach To Understanding And Benchmarking Of Cognitive And Architectural Characteristics For Neuromorphic Architectures
STATE UNIV OF NEW YORK AT BINGHAMTON BINGHAMTON United States
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This research analyzed, evaluated, and characterized the computing and energy requirements for next-generation autonomous systems of significance to DoDs mission-essential tasks and thus investigated the effective and efficient computational intelligence approaches capable of supporting desired autonomy. This assessment will help determine the processing flow of an autonomous system from the cognitive perspectives, as well as the desired performance and energy requirements from the computing perspectives. Specifically, this study first outlined the necessary cognitive primitives and processing flow for a flexible autonomous system capable of real-time problem solving. Then, it focused on the autonomous target tracking problem and explored multiple computational intelligence methods, including artificial neural network ANN, reservoir computing RC, and deep learning DL architectures, to achieve the desired autonomy. Third, it investigated the computational characteristics of those intelligence models, assessed the performance metrics in terms of accuracy, speed, and energy consumption, characterized performance and energy requirements according to the scope of the problem, as well as identified the most suitable solutions fitting into the cognitive processing flow. Finally, it explored bio-inspired dynamic ensembles of reservoir networks for multiple pattern recognition, category learning driven classification network, and evolutionary adaptation of reservoir network optimization.