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Research Paper | Computer Science & Engineering | India | Volume 13 Issue 8, August 2024 | Rating: 4.9 / 10
Advanced Computation Techniques for Complex AI Algorithms
Mohammed Saleem Sultan [8] | Mohammed Shahid Sultan [8]
Abstract: The rapid growth in Artificial Intelligence demands the progress of advanced computation techniques to support new complex algorithms. Traditional computational methods, mainly relying on classical architectures, become inadequate to satisfy the needs of state - of - the - art AI applications which demand greater computing power and efficiency. The paper deals with advanced computation techniques and their applicability and effectiveness for optimization in AI algorithms. It focuses on quantum computing, distributed systems, and neuromorphic computing as computational paradigms. Quantum computing uses principles of superposition and entanglement to give exponential speedups for specific problems of search and factorization. Distributed systems run enormous datasets and complex computations across a large number of computing resources and provide a scalable solution with efficiency. Neuromorphic computing works like the neural structure of the human brain and performs real - time processing in an energy - efficient manner. In this paper, we present a set of experiments that reveal how these cutting - edge computing technologies greatly improve the performance of AI algorithms. Quantum algorithms run on a simulated quantum processor exhibited marked computational time decreases for search and factorization problems. Distributed neural networks trained on a Hadoop cluster showed linear scalability with the addition of nodes, thus decreasing training time. That is to say, utilizing spiking neural networks allowed neuromorphic hardware to realize real - time processing while consuming very minimal energy, hence outperforming traditional architectures on tasks such as image recognition. The research builds on a number of unique data sets, with graphs showing how computational performance may be improved. In summary, our findings suggest that the marriage of these advanced computation techniques can empower the creation of more efficient and scalable AI systems and thus outline the future course of developments in the domain. This paper discusses the insights from the findings and future research directions. This paper explores advanced computation techniques crucial for optimizing complex AI algorithms, focusing on quantum computing, distributed systems, and neuromorphic computing. Through simulated experiments on quantum processors, distributed neural networks, and neuromorphic hardware, the research demonstrates significant improvements in processing speed, scalability, and energy efficiency. The findings suggest that integrating these techniques can lead to the development of more efficient and scalable AI systems, with significant implications for future AI advancements. This research is significant as it highlights the potential of cuttingedge computation techniques to revolutionize AI by improving processing efficiency, scalability, and energy consumption, paving the way for more robust and capable AI systems.
Keywords: Artificial Intelligence, Advanced Computation, Quantum Computing, Distributed Systems, Neuromorphic Computing, Scalability, Optimization
Edition: Volume 13 Issue 8, August 2024,
Pages: 934 - 939