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Neural Network Adaptations to Hardware Implementations
Type of publication: Book chapter
Citation: Moerland-97.1
Booktitle: Handbook of Neural Computation
Year: 1997
Publisher: Institute of Physics Publishing and Oxford University Publishing
Address: New York
Note: IDIAP-RR 97-17
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 nonuniformities and nonideal 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.
Userfields: ipdmembership={learning},
Projects Idiap
Authors Moerland, Perry
Fiesler, Emile
Editors Fiesler, Emile
Beale, R.
Added by: [UNK]
Total mark: 0
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