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AI for Computer Architecture [electronic resource] : Principles, Practice, and Prospects / by Lizhong Chen, Drew Penney, Daniel Jiménez.

By: Chen, Lizhong [author.].
Contributor(s): Penney, Drew [author.] | Jiménez, Daniel [author.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Synthesis Lectures on Computer Architecture: Publisher: Cham : Springer International Publishing : Imprint: Springer, 2021Edition: 1st ed. 2021.Description: XVII, 124 p. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783031017704.Subject(s): Electronic circuits | Microprocessors | Computer architecture | Electronic Circuits and Systems | Processor ArchitecturesAdditional physical formats: Printed edition:: No title; Printed edition:: No title; Printed edition:: No titleDDC classification: 621.3815 Online resources: Click here to access online
Contents:
Preface -- Acknowledgments -- Introduction -- Basics of Machine Learning in Architecture -- Literature Review -- Case Studies -- Analysis of Current Practice -- Future Directions of AI\nobreakspace { -- Conclusions -- Bibliography -- Authors' Biographies.
In: Springer Nature eBookSummary: Artificial intelligence has already enabled pivotal advances in diverse fields, yet its impact on computer architecture has only just begun. In particular, recent work has explored broader application to the design, optimization, and simulation of computer architecture. Notably, machine-learning-based strategies often surpass prior state-of-the-art analytical, heuristic, and human-expert approaches. This book reviews the application of machine learning in system-wide simulation and run-time optimization, and in many individual components such as caches/memories, branch predictors, networks-on-chip, and GPUs. The book 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 computer architecture designs.
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Preface -- Acknowledgments -- Introduction -- Basics of Machine Learning in Architecture -- Literature Review -- Case Studies -- Analysis of Current Practice -- Future Directions of AI\nobreakspace { -- Conclusions -- Bibliography -- Authors' Biographies.

Artificial intelligence has already enabled pivotal advances in diverse fields, yet its impact on computer architecture has only just begun. In particular, recent work has explored broader application to the design, optimization, and simulation of computer architecture. Notably, machine-learning-based strategies often surpass prior state-of-the-art analytical, heuristic, and human-expert approaches. This book reviews the application of machine learning in system-wide simulation and run-time optimization, and in many individual components such as caches/memories, branch predictors, networks-on-chip, and GPUs. The book 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 computer architecture designs.

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