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NeuroSOFM: A Neuromorphic Self-Organizing Feature Map Heterogeneously Integrating RRAM and FeFET
IEEE Journal on Exploratory Solid-State Computational Devices and Circuits ; 2021.
Article in English | Scopus | ID: covidwho-1483755
ABSTRACT
Many currently available hardware implementations of the unsupervised self-organizing feature map (SOFM) algorithm utilize 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 FeFETs and gated-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. Additionally, the proposed architecture is completely parallelized and provides a power efficient platform for implementing the SOFM algorithm. Author

Full text: Available Collection: Databases of international organizations Database: Scopus Language: English Journal: IEEE Journal on Exploratory Solid-State Computational Devices and Circuits Year: 2021 Document Type: Article

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Full text: Available Collection: Databases of international organizations Database: Scopus Language: English Journal: IEEE Journal on Exploratory Solid-State Computational Devices and Circuits Year: 2021 Document Type: Article