As the complexity of modern server processors increases so the validation challenges. Current design validation methods cover less, resulting in bug escape and more regress postsilicon validation. The biggest problem is manual debugging of several failures by large number of test cases. By using machine learning in server validation, validation efforts and resource requirement will reduce. Validation of future generation server will be done through the learning set generated from the previous generation device, which is a set of test cases being passed.

Protocol Validation is an important step in validating the I/O interface of the System on Chip (SoC). In developing the protocol compliance solutions for validating the Secure Digital/Embedded Multi Media Controller (SD/eMMC) protocol, the main intention is to capture the behavior of protocol under various physical conditions using a Logic Analyzer (LA). LA facilitates more capture of data and offers more number of channels and is more cost effective compared to existing Digital Storage Oscilloscope (DSO) methodology. In addition to decode, the solution is capable of carrying out the analysis of the data captured and decoded from LA. Analysis functionality includes command-response analysis, cyclic redundancy check, integrity check and flow analysis that assess and reports the functionality and performance of the protocol under variations of temperature & voltage conditions. Analyzer helps in tracing out all kind of deviations from the specifications and standards defined for SD and eMMC.

After the advent of Quantum Physics, new opportunities were created for research and development in the world of science and technology. Quantum computation is one such emerging technology which allows for notable speed-ups compared to conventional computational methods. Here a quantum bit simulator is presented. This simulator is an engineering work, and no deep understanding of quantum mechanics is required from the user. The well-known phase estimation algorithm is presented using this simulator. The qubit simulator will be a useful tool for the study of quantum computer circuits, quantum computing, and the development of new quantum algorithms

Age of human can be inferred by distinct patterns emerging from the facial appearance. Humans can easily distinguish which person is elder and which is older between two persons. When inferring a person's age, the comparison is done with his/her face and with many people whose ages are known, resulting in a series of comparative series, and then judgment is done based on the comparisons. The computer based age classification has become particularly prevalent topics recently. In this paper age classification is done by using Support Vector Machine technique. In variety of applications SVM has achieved excellent generalization performance.

In the present scenario, a server rack has multiple platforms attached to it, each designed to perform a different set of actions, thus, having different hardware requirements. To increase the throughput of such a platform either the hardware requirements are multiplied or the platform is replaced completely. This unoptimized method is rather expensive and inefficient.[1] This paper focuses on improving the performance of a system by providing accurate analysis and predict hardware requirements to improve overall throughput. For this, data logs are collected over a period of time which take performance data dumps of sensors connected to the platform via BMC. These sensors monitor the platform and measure its internal physical parameters. This data is then used to create a database and a training set. This set is used to train a machine learning algorithm which gives an efficient algorithm to analyze the present performance and give accurate prediction. This gives an optimal solution to increase throughput of a platform.[2] General TermsMachine learning, data logs, BMC, performance analysis and IPMI.