AI-ASSISTED MEMORY AND CACHE OPTIMIZATION IN MODERN OPERATING SYSTEMS AND MULTICORE PROCESSORS
DOI:
https://doi.org/10.34218/IJCET_16_02_018Keywords:
Artificial Intelligence (AI), Memory Management, Cache Optimization, Machine Learning, Deep Learning, Reinforcement Learning, Multicore Processors, Operating Systems, Cache Hit Rate, Memory Latency, Power EfficiencyAbstract
Memory and cache optimization are crucial in various areas of computing, including multicore processors and operating systems handling diverse workloads. Traditional memory management techniques such as Least Recently Used (LRU) and First-In First-Out (FIFO) are still widely used today. However, these methods do not adapt to changing workload patterns, resulting in inefficiencies such as increased cache misses and memory latency. Artificial Intelligence (AI) offers a promising solution to memory and cache management by dynamically adapting to workload variations, thereby enabling maximum resource utilization. Studies have shown that AI-based optimization can lead to a 40% reduction in memory latency, a 15–20% increase in cache hit rates, and lower energy consumption. These improvements make AI particularly well-suited for modern high-performance computing platforms. AI can enhance computational efficiency, eliminate bottlenecks, and improve system responsiveness. Future research should focus on real-time implementation of AI models, hybrid techniques combining heuristic and AI-driven approaches, and ensuring the security of AI-based memory management systems. As AI continues to evolve, its role in optimizing computing architectures will expand significantly, paving the way for smarter and more efficient memory and cache management solutions.
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Copyright (c) 2025 Vasuki Shankar, Nanditha Muralidhar, Vinay Krishnamurthy (Author)
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