UC Irvine

The ever-increasing demands of data-intensive applications and the rapid evolution of computer architectures have posed significant challenges in memory performance and energy efficiency. Efficient memory management is crucial to meet the requirements of these applications while optimizing the utilization of memory resources. Traditional approaches that rely on workload-specific optimizations and static memory configurations are no longer sufficient to address the dynamic nature of modern computing systems.

To overcome these challenges, the concept of computational self-awareness (CSA) has emerged as a promising approach. Computational self-awareness draws inspiration from psychology and neuroscience and aims to develop intelligent systems that can learn from past experiences, reason about their current state, and make informed decisions at runtime.

In this thesis, I explore the application of computational self-awareness in the context of memory management. I investigate the different degrees of self-awareness applied across the memory subsystem and examine their benefits on memory performance and energy consumption. The results highlight the potential of computational self-awareness in addressing the challenges posed by data-intensive applications and evolving computer architectures, paving the way for improved performance, energy efficiency, and bandwidth utilization in memory systems.