Ferroelectric Memristive Networks for Dimensionality Reduction: A Process for Effectively Classifying Cancer Datasets
Sign Up to like & getrecommendations! Published in 2019 at "Integrated Ferroelectrics"
DOI: 10.1080/10584587.2019.1668697
Abstract: Abstract In this work, a copper-doped (5%) zinc oxide (Cu:ZnO) ferroelectric materials-based memristor model was realized and it was employed to develop principal component analysis (PCA), a data dimension reduction technique. The developed PCA was… read more here.
Keywords: cancer datasets; reduction; network; memristive networks ... See more keywords
Designing Efficient Bit-Level Sparsity-Tolerant Memristive Networks.
Sign Up to like & getrecommendations! Published in 2023 at "IEEE transactions on neural networks and learning systems"
DOI: 10.1109/tnnls.2023.3250437
Abstract: With the rapid progress of deep neural network (DNN) applications on memristive platforms, there has been a growing interest in the acceleration and compression of memristive networks. As an emerging model optimization technique for memristive… read more here.
Keywords: memristive networks; sparsity tolerant; level sparsity; bit level ... See more keywords