Venkateswarlu chennareddy - Academia.edu (original) (raw)
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Papers by Venkateswarlu chennareddy
IEEE, 2024
As data continues to grow exponentially, the processing and generation of foundational data requi... more As data continues to grow exponentially, the processing and generation of foundational data required for Artificial Intelligence (AI) models becomes an expensive task. This paper introduces a method to minimize the dataset required for these models by combining a custom Reservoir Sampling algorithm with an automated, independent, self-adaptive comprehensive data test module. This approach produces a minimized dataset that maintains model accuracy while ensuring essential coverage of business scenarios with the help of a comprehensive data test suite. This method also ensures the integrity and representativeness of the data required for business use cases and promises a dataset reduction, with an estimated optimization of around 80%. This approach serves as a key resource for organizations aiming to enhance their data handling strategies, ensuring both model accuracy and the comprehensiveness of the data required for AI applications.
2006 International Conference on Advanced Computing and Communications, Dec 1, 2006
A specifi cation and verifi cation methodology for Distributed Shared Memory (DSM) consistency mo... more A specifi cation and verifi cation methodology for Distributed Shared Memory (DSM) consistency models specifi cally weak consistency model is proposed. For this, we designed and implemented abstract DSM System. In DSM system, sequential consistency unnecessarily reduces the performance of the system because it does not allow to reorder or pipeline the memory operations. Relaxed memory consistency allows reordering of memory events and buffering or pipelining of memory accesses. So that relaxed consistency improves the performance of the DSM system. For any critical system, it is very important to develop methods that increase our confi dence in the correctness of such systems. One of such methods for checking the correctness of critical system is formal verifi cation. For verifi cation of weak consistency models we specify the weak consistency properties and are verifi ed on Abstract DSM System using CADP Tool box.
IEEE, 2024
As data continues to grow exponentially, the processing and generation of foundational data requi... more As data continues to grow exponentially, the processing and generation of foundational data required for Artificial Intelligence (AI) models becomes an expensive task. This paper introduces a method to minimize the dataset required for these models by combining a custom Reservoir Sampling algorithm with an automated, independent, self-adaptive comprehensive data test module. This approach produces a minimized dataset that maintains model accuracy while ensuring essential coverage of business scenarios with the help of a comprehensive data test suite. This method also ensures the integrity and representativeness of the data required for business use cases and promises a dataset reduction, with an estimated optimization of around 80%. This approach serves as a key resource for organizations aiming to enhance their data handling strategies, ensuring both model accuracy and the comprehensiveness of the data required for AI applications.
2006 International Conference on Advanced Computing and Communications, Dec 1, 2006
A specifi cation and verifi cation methodology for Distributed Shared Memory (DSM) consistency mo... more A specifi cation and verifi cation methodology for Distributed Shared Memory (DSM) consistency models specifi cally weak consistency model is proposed. For this, we designed and implemented abstract DSM System. In DSM system, sequential consistency unnecessarily reduces the performance of the system because it does not allow to reorder or pipeline the memory operations. Relaxed memory consistency allows reordering of memory events and buffering or pipelining of memory accesses. So that relaxed consistency improves the performance of the DSM system. For any critical system, it is very important to develop methods that increase our confi dence in the correctness of such systems. One of such methods for checking the correctness of critical system is formal verifi cation. For verifi cation of weak consistency models we specify the weak consistency properties and are verifi ed on Abstract DSM System using CADP Tool box.