A Survey of Machine Learning Applied to Computer Architecture Design

TL;DR

This survey reviews how machine learning techniques are increasingly applied to various aspects of computer architecture, showing promising results and highlighting future research directions for more automated and optimized design processes.

Contribution

It provides a comprehensive overview of machine learning applications in computer architecture, analyzing current practices, identifying effective strategies, and suggesting future research avenues.

Findings

ML strategies often outperform traditional methods

ML is applied across simulation, optimization, and component design

Future work can expand ML's role in architecture automation

Abstract

Machine learning has enabled significant benefits in diverse fields, but, with a few exceptions, has had limited impact on computer architecture. Recent work, however, has explored broader applicability for design, optimization, and simulation. Notably, machine learning based strategies often surpass prior state-of-the-art analytical, heuristic, and human-expert approaches. This paper reviews machine learning applied system-wide to simulation and run-time optimization, and in many individual components, including memory systems, branch predictors, networks-on-chip, and GPUs. The paper further analyzes current practice to highlight useful design strategies and identify areas for future work, based on optimized implementation strategies, opportune extensions to existing work, and ambitious long term possibilities. Taken together, these strategies and techniques present a promising future for increasingly automated architectural design.