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• W3206233478 abstract "Many currently available hardware implementations of the unsupervised self-organizing feature map (SOFM) algorithm utilize complementary metal–oxide–semiconductor (CMOS)-only circuits that often compromise key behaviors of the SOFM algorithm due to complexity. We propose a neuromorphic architecture harnessing the unique properties of ferroelectric field-effect transistors (FeFETs) and gated-resistive random access memory (RRAM) for in-memory computing to implement the SOFM algorithm. The FeFET-based synapse, organized in a novel circuit, is able to compute the input-weight Euclidean error in memory via the saturation drain current. The self-decaying states of the gated-RRAM allow for a self-decaying neighborhood and learning rate implementation to allow for convergence and lifelong learning. This novel architecture is able to successfully cluster benchmarks (RGB colors and MNIST handwritten digits) and real-life datasets, such as COVID-19 patient chest X-rays completely unsupervised. The architecture also demonstrates a significant amount of robustness to device variability and damaged neurons. In addition, the proposed architecture is completely parallelized and provides a power-efficient platform for implementing the SOFM algorithm." @default.
• W3206233478 created "2021-10-25" @default.
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• W3206233478 date "2021-12-01" @default.
• W3206233478 modified "2023-10-14" @default.
• W3206233478 title "NeuroSOFM: A Neuromorphic Self-Organizing Feature Map Heterogeneously Integrating RRAM and FeFET" @default.
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• W3206233478 doi "https://doi.org/10.1109/jxcdc.2021.3119489" @default.
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