By J. M. Cruz, L. O. Chua (auth.), Leon O. Chua, Glenn Gulak, Edmund Pierzchala, Angel Rodríguez-Vázquez (eds.)
Cellular Neural Networks and Analog VLSI brings jointly in a single position vital contributions and up to date learn ends up in this fast-paced zone.
Cellular Neural Networks and Analog VLSI serves as a superb reference, supplying perception into the most tough learn matters within the field.
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Extra resources for Cellular Neural Networks and Analog VLSI
I ! I I, ' Vu ( to nelg/lbour eels ) Fig. 1. CMOS implementation of the programmable CNN cell. 7 cells per square millimeter is obtained with 11 programmable parameters (9 feedback parameters, one control parameter and the offset term). The lateral bipolar transistors occupy about 25% of the cell area. 2ftm BICMOS process of the same foundry). Only one metal layer has been used for interconnections leaving the second metal layer to protect the circuitry from the incident light. Therefore, an additional metal layer would greatly reduce the cell area due to the large area devoted to interconnection in the present implementation, as can be seen in the layout in figure 2.
Propagating templates without input-templates with a non-zero A and a zero B template-use the input image as the initial state and during their computation, this original input is not stored in the network. Consequently this class of templates inherently has a much higher susceptibility to dynamic errors. The connected component detector and the averaging template are ex- 29 254 P. Kinget and M. Steyaert amples of such templates . A. We can summarize that the effect of random dynamical errors on the correct operation of CNN's is very small.
C Systematic Static Errors and the Application of Tuning Techniques Systematic errors are caused by deterministic processes and are the same for every cell. The non-linearity of device characteristics can result in a signal-dependent behavior of the cell circuit blocks and can introduce distortion errors. These errors can be eliminated by correct biasing, good signal amplitude choices, or nonlinearity cancellation schemes like differential or translinear circuit techniques. Random and systematic static errors differ in nature of their cause but their impact on the network behavior can be evaluated with a similar methodology.