Programmable CMOS CNN Cell Based on Floating-gate Inverter Unit |
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| Authors: | Jesus E. Molinar-Solis Felipe Gomez-Castaneda Jose A. Moreno-Cadenas Victor H. Ponce-Ponce |
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| Affiliation: | (1) Mexico-State Autonomous University (UAEM-Ecatepec), Jose Revueltas 17 Col. Tierra Blanca, Ecatepec de Morelos, Estado de Mexico, Mexico, C.P. 55020;(2) Electrical Engineering Department, Center for Research and Advanced Studies (CINVESTAV-IPN), Av. Instituto Politecnico Nacional, No. 2508, Col. San Pedro Zacatenco, Del. G.A. Madero, C.P. 07360 Mexico, Mexico |
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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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| Keywords: | vision chips cellular neural network floating-gate devices |
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