Neuromorphic or bioinspired computational platforms, as an alternative for von-Neumann structures, have benefitted from the excellent features of emerging technologies in order to emulate the behavior of the biological brain in an accurate and energy-efficient way. Integrability with CMOS technology and low power consumption make ferroelectric field-effect transistor (FEFET) an attractive candidate to perform such paradigms, particularly for image processing. In this article, we use the FEFET device to make energy-efficient oscillatory neurons as the main parts of neural networks for image processing applications, especially for edge detection. Based on our simulation results, we estimated a significant energy efficiency compared with other technologies, which shows roughly 5−120× reduction, depending on the design.
|Number of pages||8|
|Journal||IEEE Journal on Exploratory Solid-State Computational Devices and Circuits|
|Early online date||29 Sep 2020|
|Publication status||Published - Dec 2020|