Title :
Efficient Hamming weight comparators of binary vectors
Author_Institution :
Lab. LICM, Univ. of Metz, Metz
Abstract :
New comparators of the Hamming weight of binary vectors built using threshold circuits are proposed. One version compares the Hamming weight of a binary vector to a fixed threshold, whereas the other compares the Hamming weights of two independent vectors. Their applications include digital neural networks, pattern matching and median/rank filters.
Keywords :
comparators (circuits); median filters; network synthesis; neural nets; pattern matching; vectors; Hamming weight comparators; binary vectors; digital neural networks; median-rank filters; pattern matching;
Journal_Title :
Electronics Letters
DOI :
10.1049/el:20070141
