Journal article
Elementary function generators for neural-network emulators
IEEE transactions on neural networks, Vol.11(6), pp.1438-1449
11/2000
Handle:
https://hdl.handle.net/2376/108187
PMID: 18249867
Abstract
Piecewise first- and second-order approximations are employed to design commonly used elementary function generators for neural-network emulators. Three novel schemes are proposed for the first-order approximations. The first scheme requires one multiplication, one addition, and a 28-byte lookup table. The second scheme requires one addition, a 14-byte lookup table, and no multiplication. The third scheme needs a 16-byte lookup table, no multiplication, and no addition. A second-order approximation approach provides better function precision; it requires more hardware and involves the computation of one multiplication and two additions and access to a 28-byte lookup table. We consider bit serial implementations of the schemes to reduce the hardware cost. The maximum delay for the four schemes ranges from 24- to 32-bit serial machine cycles; the second-order approximation approach has the largest delay. The proposed approach can be applied to compute other elementary function with proper considerations.
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Details
- Title
- Elementary function generators for neural-network emulators
- Creators
- S Vassiliadis - Dept. of Electr. Eng., Delft Univ. of Technol., NetherlandsMing Ming ZhangJ.G Delgado-Frias
- Publication Details
- IEEE transactions on neural networks, Vol.11(6), pp.1438-1449
- Academic Unit
- Electrical Engineering and Computer Science, School of
- Publisher
- IEEE
- Identifiers
- 99900547790801842
- Language
- English
- Resource Type
- Journal article