A Survey of Machine Learning for Computer Architecture and Systems

TL;DR

This survey reviews how machine learning techniques are increasingly used to improve computer architecture and systems design, highlighting current methods, problems addressed, and future opportunities for integration.

Contribution

It provides a comprehensive taxonomy and summary of ML applications in architecture and systems, including design automation and future research directions.

Findings

ML techniques are used for predictive modeling and design methodology.

Common problems include performance prediction and optimization.

Future opportunities involve more integrated ML-driven design processes.

Abstract

It has been a long time that computer architecture and systems are optimized for efficient execution of machine learning (ML) models. Now, it is time to reconsider the relationship between ML and systems, and let ML transform the way that computer architecture and systems are designed. This embraces a twofold meaning: improvement of designers' productivity, and completion of the virtuous cycle. In this paper, we present a comprehensive review of the work that applies ML for computer architecture and system design. First, we perform a high-level taxonomy by considering the typical role that ML techniques take in architecture/system design, i.e., either for fast predictive modeling or as the design methodology. Then, we summarize the common problems in computer architecture/system design that can be solved by ML techniques, and the typical ML techniques employed to resolve each of them. In addition to emphasis on computer architecture in a narrow sense, we adopt the concept that data centers can be recognized as warehouse-scale computers; sketchy discussions are provided in adjacent computer systems, such as code generation and compiler; we also give attention to how ML techniques can aid and transform design automation. We further provide a future vision of opportunities and potential directions, and envision that applying ML for computer architecture and systems would thrive in the community.