Exact(10)
Each architecture is described and evaluated using an underwater simulator with an AUV model.
Each architecture is represented using a matrix of functions versus products, with shared/unique function levels indicated.
Then each architecture is evaluated according to their generalization capability and capability to conform to original data.
Each architecture is well-suited to solve a particular task efficiently, which is proven by a detailed evaluation.
Each architecture is evaluated with respect to its suitability for the paradigm of the dynamic and partial reconfiguration in FPGA implementations.
This work presents the consumption analysis of two applications, each of them built with two different architectures in order to identify under which situation each architecture is more efficient.
Similar(50)
Limitations of each architecture were identified.
Models of each architecture were developed to give insight into the performance and losses of each of the options.
Each node architecture is similar to that of a single-node reconfigurable computing solution.
Each system architecture is layered and service oriented, consisting of the following main components: data sources, data registration, data access, harvesting and transformation, indexing and catalog ingestion, catalog search and data download.
Each model architecture is estimated k = 20 times, and because there are eight different neural network-based model architecture to be estimated in the study, the total number of estimated models is 160; whereas, the selected 8 models (based on the lowest MSE error criteria) will be reported in the study.
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