Leveraging AI for Optimized Memory Management in C/C++ Applications

Presenter

James K. Roberts

Campus

Bunker Hill Community College

Sponsor

Paul Kasili, Department of Biology and Chemistry, Bunker Hill Community College

Schedule

Session 2, 11:30 AM - 12:15 PM [Schedule by Time][Poster Grid for Time/Location]

Location

Poster Board A12, Campus Center Auditorium, Row 1 (A1-A20) [Poster Location Map]

Abstract

Today, C/C++ is considered a low-level language since it provides minimal abstraction between the written code and the computer hardware. One of the specific tasks C/C++ developers must undertake is the management of their memory. Higher-level languages such as Java, Python, and JavaScript include garbage collectors, which automatically track and dispose of objects in memory when they have no more references. However, C/C++ does not include garbage collection. C++ includes Resource Allocation Is Initialization (RAII), though it is not comparable to garbage collection.

Nevertheless, C/C++ developers still look for methods to simplify their memory management, such as through std::unique_ptr or std::shared_ptr. Along with memory management, there is the concern of memory optimization. Along with tracking the memory usage, developers need to ensure their code can run efficiently on whatever hardware they are programming. With advancements in the development of Artificial Intelligence (AI) and large-language models that exist today, this research project involved the production of a small, utility-focused AI that can offload the task of memory management and choosing a memory management model that is optimal for the data being stored, and for the frequency of which the data is being accessed. A model for how this AI could be trained, structured, and built, along with original research into the development of this AI model is presented in this work. Finally, a review of the current research discussing the inclusion of small-AI systems in languages that require manual memory management will be presented.

Keywords

small ai, memory management, memory optimization

Research Area

Artificial Intelligence

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