| Summary: | 博士 === 國立交通大學 === 電子工程系所 === 97 === This dissertation focuses on the studies and applications of the cellular neural/nonlinear networks (CNN). CNN is an analog CPU array which can imitate the operations of neural connections which is suitable for image processing. Although the speed of the recent digital CPUs can reach higher than several GHz, when the digital CPU is applied on the image processing, it takes a lot of time to achieve the processing separately. Hence, the advantage of parallel processing of CNN array is required to achieve high speed processing. According to the properties of CNN, two major topics are realized by using analog circuit design.
I.The design and analysis of a CMOS low-power, large-neighborhood CNN with propagating connections
II.The design and analysis of a ratio memory CNN
Recently, cellular nonlinear network universal machine (CNNUM) can only achieve the 3x3 templates of nearest connecting correlations. The main concept of large-neighborhood cellular nonlinear network (LNCNN) is to extend the connecting correlations and to increase the capability of CNN. Moreover, some studies have decomposed the LNCNN templates into several 3x3 templates to realize the same functions. However, this may take more cost to achieve one LNCNN function. Hence, it is necessary to design a LNCNN for the templates larger than 3x3.
Because LNCNN is a very large scale array, the power consumption and chip area are considered first. With the propagating connections, the functions of LNCNN are realized by the designed 20x20 LNCNN array and the chip size is 1543 um x 1248 um. The power consumption is 0.7 mW on standby and 18 mW in operation with a system clock frequency of 20 MHz.
The purpose of the learnable ratio memory cellular nonlinear networks is to learn the every kind of patterns and recover the learned noisy patterns. The concept is to store the correlations of two neighboring cells on the capacitor in the ratio memories and use the intrinsic leakage to enhance the common characteristics. Moreover, the templates are normalized by the correlation with neighboring cells to increase the recognition rate and thus, it is called ratio memory. However, due to the difference of any two cells, if the same elapsed time for leakage is applied to enhance the characteristics, it may cause only the self-feedback term to remain or the enhancement of common characteristics to be smaller. Hence, the templates are decided by the correlation and the mean of the four correlations around one cell. This can make the design much easier and the divider can be abandoned. Besides, by the deviation of the statistics and probability, there exists a dc term except for the templates. It is found that the threshold template is required and learned by recursive learning to gather the information of the noisy patterns to increase the recognition rate.
The main contribution of this dissertation is that the complete architecture of large-neighborhood CNN has been established and realized by a simple circuit design. Hence, a small-size, low-power LNCNN chip has been fabricated and measured. According to the experimental result, the LNCNN chip can be applied on the binary image processing. Moreover, the statistic and probability models of the learnable ratio memory CNN has also been derived and, according to the results, the learning of the threshold templates are used to increase the recognition rate. Furthermore, the learnable ratio memory CNN without elapsed time has also been proposed to simplify the complexity of the circuits for realization.
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