ML for System-Level Modeling

Erika S. Alcorta (), Philip Brisk () and Andreas Gerstlauer ()
Additional contact information
Erika S. Alcorta: The University of Texas at Austin
Philip Brisk: University of California
Andreas Gerstlauer: The University of Texas at Austin

Chapter Chapter 18 in Machine Learning Applications in Electronic Design Automation, 2022, pp 545-579 from Springer

Abstract: Abstract Ever-increasing complexity and heterogeneity of systems and applications pose fundamental new challenges for design, programming, and runtime management of compute systems. Fast and accurate models of architectures and the applications running on them are essential for early design space exploration and hardware/software co-development. Traditional simulation-based or analytical models are often too slow or inaccurate to effectively support design processes. Machine learning for system-level modeling has emerged to bridge the gap between slow simulation-based methodologies and inaccurate analytical models. The work in this space is large and diverse, making predictions for different aspects of the system. We define a three-dimensional space to differentiate existing work in system-level models based on their inputs and target predictions: (1) Cross-layer models predict low-level architectural details based on high-level features, allowing architects to estimate power and performance of real-world applications that are too complex to run on a detailed simulator; (2) cross-platform models learn the relationship of program executions on different platforms, allowing programmers to profile their code on a development machine and estimate the expected behavior on other machines; and (3) runtime workload models forecast future workload behaviors by looking at their execution history, enabling proactive runtime resource management. In this chapter, we describe representative works in each dimension and discuss their trade-offs and accuracies.

Keywords: System-level design; Machine learning; Power and performance modeling; Cross-platform prediction; Workload forecasting; Workload phase prediction; Virtual platform prototyping (search for similar items in EconPapers)
Date: 2022
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-031-13074-8_18

Ordering information: This item can be ordered from
http://www.springer.com/9783031130748

DOI: 10.1007/978-3-031-13074-8_18

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().