A few decades ago, communication inside the chip is done by transferring signals between the cores. This conventional method is not worthy because of the increase in latency and power consumption. To rectify this issue Network-on-Chip (NoC) technology has emerged. NoC technology is invented to transfer data packets instead of signals. Machine Learning NoC (M/LNoC) is a very fast-growing technology in today’s Integrated Circuit world for the communication between Intellectual property (IP) cores. The machine learning algorithms are used in the existing and emerging novel NoCs. In this paper, various evolving NoC technologies to decrease the transfer latency, power consumption of the IC is addressed for the implementation of the machine learning algorithm. The NoCs working with machine learning algorithms are called M/L NoC. We also provided the security issues to be focused- on in the M/L NoC. Also, we have provided the available NoC tools for the NoC researchers.
Fetus weight at various stages of pregnancy is a critical component in determining the health of the baby. Abnormalities arising early in the pregnancy may be prevented by preventive measures. A variety of techniques suggested to predict foetus weight. Computer vision is a capability that can estimate the weight of a baby based on ultra-sonograms taken at various stages of pregnancy. Using the scanned data, one may train an advanced convolutional neural network that helps in accurately forecasting the fetus's size, weight, and overall health. The research utilizes computer vision techniques with image clustering methods for preprocessing, to predict the foetus's health, training datasets defective foetus datasets and healthy foetus datasets. Developing an integrated computer vision and a deep neural network is the hour which decrease the cost of operations and manual processes This study estimate the fetus's weight with optimal accuracy range at varying gestation age.
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