A Survey of Machine Learning Applied to Computer Architecture Design
This survey identifies opportunities for automating architectural design, which could benefit researchers and engineers in computer architecture, though it is incremental as it synthesizes existing work.
The paper reviews the application of machine learning across computer architecture design, highlighting that ML-based strategies often surpass traditional analytical, heuristic, and human-expert approaches in areas like simulation, optimization, and component design.
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.