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- W1594166549 abstract "Artificial neural networks have been effectively applied to a variety of medical diagnostic and classification situations. More recently, there is a growing interest in implementing such algorithms for real-time monitoring or fast-time scanning in the classification of normal and abnormal. These applications will benefit from hardware implementation. It is well known that the input and weights precision has a considerable impact on the circuit's complexity, speed and power consumption. This paper evaluates two training methods for selecting limited precision weights for ballistocardiogram classification and suggests that a 7-bit weight precision can provide a similar performance compared with a high precision weights expression. Furthermore, a method is proposed for implementing the artificial neural networks using field programmable gate arrays for the rapid prototyping of algorithm specific hardware." @default.
- W1594166549 created "2016-06-24" @default.
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- W1594166549 date "2002-11-19" @default.
- W1594166549 modified "2023-09-23" @default.
- W1594166549 title "The selection of weights precision for ballistocardiography classification" @default.
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- W1594166549 doi "https://doi.org/10.1109/icnn.1995.487752" @default.
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