DocumentCode :
3394760
Title :
An encoder for vector quantization neural networks
Author :
Ancona, Fabio ; Rovetta, Stefano ; Zunino, Rodolfo
Author_Institution :
Dept. of Biophys. & Electron. Eng., Genoa Univ., Italy
Volume :
2
fYear :
1997
fDate :
3-6 Aug. 1997
Firstpage :
1286
Abstract :
Large-scale parallelism and analog computation are exploited to obtain a neural module, suitable for both functioning and training, since appropriate signal lines are provided. The VQ encoder is self-contained and therefore can be embedded into any system, either analog or digital. It implements efficiently the vector matching operations, therefore it can be exploited in systems based on any vector quantization algorithm, with good throughput.
Keywords :
VLSI; analogue processing circuits; neural chips; vector quantisation; VLSI; analog computation; embedded system; large-scale parallelism; neural module; signal lines; throughput; vector matching operations; vector quantization neural networks; Biophysics; Circuits; Large-scale systems; Neural networks; Neurons; Parallel processing; Prototypes; Throughput; Vector quantization; Very large scale integration;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Circuits and Systems, 1997. Proceedings of the 40th Midwest Symposium on
Print_ISBN :
0-7803-3694-1
Type :
conf
DOI :
10.1109/MWSCAS.1997.662316
Filename :
662316
Link To Document :
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