TY - JOUR
T1 - Programmable CMOS CNN cell based on floating-gate inverter unit
AU - Molinar-Solis, Jesus E.
AU - Gomez-Castaneda, Felipe
AU - Moreno-Cadenas, Jose A.
AU - Ponce-Ponce, Victor H.
PY - 2007/10
Y1 - 2007/10
N2 - 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.
AB - 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.
KW - Cellular neural network
KW - Floating-gate devices
KW - Vision chips
UR - http://www.scopus.com/inward/record.url?scp=35148814789&partnerID=8YFLogxK
U2 - 10.1007/s11265-007-0056-7
DO - 10.1007/s11265-007-0056-7
M3 - Artículo
AN - SCOPUS:35148814789
SN - 1387-5485
VL - 49
SP - 207
EP - 216
JO - Journal of VLSI Signal Processing Systems for Signal, Image, and Video Technology
JF - Journal of VLSI Signal Processing Systems for Signal, Image, and Video Technology
IS - 1
ER -