Programmable CMOS CNN Cell Based on Floating-gate Inverter Unit


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Abstract

At present, the Cellular Neural Network (CNN) is a potential parallel structure able to perform image processing tasks in real-time when is effectively implemented in CMOS technology. The CNN silicon integration success is due mainly to the local connectivity of processing cells. In this work, an alternative design based on floating-gate MOS inverters is presented, which uses unipolar signals for solving binary tasks. The approach brings a fast response in a reduced silicon area, as shown through electrical simulations. A prototype cell in CMOS technology (AMI, 1.2 micron) was fabricated and tested for eight image processing tasks.

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