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Neural Network Adaptations to Hardware Implementations (1997)
| Citations: | 19 - 1 self |
BibTeX
@MISC{Moerland97neuralnetwork,
author = {Perry Moerland and Emile Fiesler},
title = {Neural Network Adaptations to Hardware Implementations},
year = {1997}
}
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Abstract
In order to take advantage of the massive parallelism offered by artificial neural networks, hardware implementations are essential. However, most standard neural network models are not very suitable for implementation in hardware and adaptations are needed. In this section an overview is given of the various issues that are encountered when mapping an ideal neural network model onto a compact and reliable neural network hardware implementation, like quantization, handling non-uniformities and non-ideal responses, and restraining computational complexity. Furthermore, a broad range of hardware-friendly learning rules is presented, which allow for simpler and more reliable hardware implementations. The relevance of these neural network adaptations to hardware is illustrated by their application in existing hardware implementations.
Keyphrases
hardware implementation neural network adaptation broad range computational complexity massive parallelism standard neural network model hardware-friendly learning rule reliable neural network hardware implementation reliable hardware implementation various issue non-ideal response artificial neural network ideal neural network model
