| Autor: |
Siddharth Barve, Joshua Mayersky, Andrew J. Ford, Alexander Jones, Bayley King, Aaron Ruen, Rashmi Jha |
| Jazyk: |
angličtina |
| Rok vydání: |
2021 |
| Předmět: |
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| Zdroj: |
IEEE Journal on Exploratory Solid-State Computational Devices and Circuits, Vol 7, Iss 2, Pp 97-105 (2021) |
| Druh dokumentu: |
article |
| ISSN: |
2329-9231 |
| DOI: |
10.1109/JXCDC.2021.3119489 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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