International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 11 Issue: 07 | July 2024
p-ISSN: 2395-0072
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Reversible Data Processing in AI: Leveraging Logic Gates for Improved Machine Learning Pipelines Kalari Seetharam1, Pothuri Nagasravya2, Pathuri Suharsha3 , Perumalla V Rama Kulasekhara Chakravarthi4 Department Of Computer Science And Education, KLEF, Vadeshwaram, Andhra Pradesh, INDIA ---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - Reversible computing presents notable benefits in terms of data integrity and energy efficiency. It is typified by computer operations that are time invertible. In order to improve data processing efficiency while maintaining data integrity and minimizing redundancy, this research investigates the integration of reversible logic gates into machine learning pipelines. With the use of a variety of reversible logic gates, such as NOT, CNOT, Toffoli, and Fredkin gates, we present a thorough approach for reversible data processing. We guarantee that the transformations are entirely reversible by preprocessing the dataset with these gates, which preserves information and permits the reversal of computational steps without loss. Using the breast cancer dataset, these gates are applied to a binary classification task in the practical application. After transforming the data with a sequence of reversible logic gates in the preprocessing stage, the processed data is used to train a Random Forest classifier. The outcomes show that the reversible data processing strategy offers extra advantages in terms of data integrity in addition to preserving the machine learning model's correctness. The potential of reversible computing in AI and machine learning is demonstrated by this work, especially in situations when data integrity and energy efficiency are crucial. We describe a novel technique to building dependable and efficient machine learning pipelines by utilizing reversible logic gates. According to our research, incorporating reversible computing ideas into AI systems can result in more reliable and long-lasting data processing frameworks, creating new opportunities for AI research and development. Key Words: Reversible computing, reversible logic gates, data integrity, machine learning pipelines, Toffoli gate, CNOT gate, Fredkin gate, NOT gate, energy efficiency, redundancy reduction, AI preprocessing, binary classification, breast cancer dataset, Random Forest classifier, data processing efficiency, sustainable computing, robust AI systems, reversible data processing
I. INTRODUCTION Systems that are efficient while maintaining high levels of data integrity are becoming more and more necessary in the rapidly developing fields of artificial intelligence (AI) and machine learning. According to Landauer's principle, traditional computing techniques frequently have intrinsic irreversibility, where each processing step results in a loss of information and an increase in entropy. This irreversibility leads to higher energy consumption and the possible loss of vital data integrity, which can be harmful in vital applications where accuracy and dependability are essential, like medical diagnostics. The redundancy found in data processing pipelines can also result in inefficiencies, which exacerbates the problems AI systems confront. Reversible computing presents a viable answer to these problems. Reversible computing is a paradigm for computing in which all operations are reversible, maintaining data while it is processed. Reversible logic gates, such the Toffoli, CNOT, Fredkin, and NOT gates, are used in this method to guarantee that data alterations may be undone, preserving data integrity and allowing for energy-efficient calculations. In order to accomplish reversible data processing, we provide in this research a novel framework that incorporates these reversible logic gates into machine learning pipelines. This technology solves the inefficiencies and redundancies present in conventional data processing techniques while maintaining the correctness of the data. Reversible computing is a general notion that can be used in many computer science and engineering fields. Reversible computing has several uses, from large-scale data centers to low-power embedded systems, by lowering redundancy and preserving data integrity. This paradigm encourages the creation of computational systems that are more energy efficient, which is essential for developing technology in areas like bioinformatics, quantum computing, and cryptography. Reversing computational stages creates new opportunities for robust and sustainable hardware and software system design, laying the groundwork for further advancements in these fields. Reversible computing ideas are applied in the context of machine learning to address particular issues with redundancy and data integrity. The inefficiencies of traditional machine learning pipelines are frequently caused by irreversible transformations and the possibility of data loss. Our suggested approach guarantees that the data changes performed are reversible, protecting the integrity of the data and minimizing redundancy. It accomplishes this by integrating reversible logic gates into the preparation phase of machine learning pipelines. By using this method, the data processing pipeline is made more efficient and the accuracy and dependability of the machine learning models that were trained on the
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