The article presents methods of dealing with huge data in the domain of neural networks. The decomposition of neural networks is introduced and its efficiency is proved by the authors’ experiments. The examinations of the effectiveness of argument reduction in the above filed, are presented. Authors indicate, that decomposition is capable of reducing the size and the complexity of the learned data, and thus it makes the learning process faster or, while dealing with large data, possible. According to the authors experiments, in some cases, argument reduction, makes the learning process harder.
Back propagation (Artificial intelligence); Computer algorithms; Decomposition method; Field programmable gate arrays; Neural networks (Computer science)
Computer Engineering | Controls and Control Theory | Digital Circuits | Electrical and Computer Engineering | Signal Processing | Systems and Communications
Implementation of Large Neural Networks using Decomposition.
Las Vegas, NV: University of Nevada, Las Vegas.