國立虎尾科技大學 |

Cost Effective Machine Learning for Computer Architecture Design.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Cost Effective Machine Learning for Computer Architecture Design./
作者:	Amiraski, Maziar.
面頁冊數:	1 online resource (110 pages)
附註:	Source: Dissertations Abstracts International, Volume: 85-11, Section: A.
Contained By:	Dissertations Abstracts International85-11A.
標題:	Landscape architecture. -
電子資源:	click for full text (PQDT)
ISBN:	9798382737515

Cost Effective Machine Learning for Computer Architecture Design.
Amiraski, Maziar.

Cost Effective Machine Learning for Computer Architecture Design. - 1 online resource (110 pages)

Source: Dissertations Abstracts International, Volume: 85-11, Section: A.

Thesis (Ph.D.)--Tufts University, 2024.

Includes bibliographical references

The computer architecture design landscape is faced by considerable challenges, including the growing diversity and complexity of workloads, the increasing heterogeneity of hardware platforms, and the need to balance competing objectives such as performance, power efficiency, and reliability. The sheer size of the design space and the difficulty of optimizing across multiple dimensions present significant obstacles. Machine learning has shown the potential to address these problems and drastically change the course of computer architecture design. First, traditional heuristic approaches to system design often struggle to cope with the increasing complexity of modern workloads and hardware platforms. Machine learning techniques offer a more flexible and adaptive alternative, capable of extracting insights from vast datasets to optimize architecture parameters. Moreover, machine learning enables a shift towards more holistic optimization strategies, considering multiple objectives simultaneously and accommodating diverse design constraints.Utilizing an accurate and low cost machine learning model to design effective computer systems is the goal of this dissertation. We showcase two machine learning models for two different purposes: In the first part, we present how we designed a highly accurate classifier with low overhead to partition the last level cache (LLC). LLC being a shared resource, is faced with performance and fairness challenges when running competing workloads with diverse behaviors. CASHT demonstrates my research focus on characterizing systems under contention and collecting data for the purpose of training machine learning models. These models, while accurate, have low hardware implementation overhead (considering the area, memory and latency) and are specifically designed to be integrated into current standard platforms.In the second part, we address the pressing issue of managing advanced hotspots on modern microprocessors, emphasizing its detrimental effect on performance, product reliability, and device lifetime which is only exacerbated as we decrease the transistor sizes. We introduce Boreas, an effective control system based on a low-cost regression model to prevent hotspots on single digit process nodes. Boreas demonstrates another dimension of my research on the use of machine learning for system design by training a model to assist the DVFS controller. Boreas outperforms existing thermal management techniques while remaining lightweight and well-suited for implementation in hardware.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798382737515Subjects--Topical Terms:

555495
Landscape architecture.
Subjects--Index Terms:

Machine learningIndex Terms--Genre/Form:

554714
Electronic books.

Cost Effective Machine Learning for Computer Architecture Design.
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520 $a The computer architecture design landscape is faced by considerable challenges, including the growing diversity and complexity of workloads, the increasing heterogeneity of hardware platforms, and the need to balance competing objectives such as performance, power efficiency, and reliability. The sheer size of the design space and the difficulty of optimizing across multiple dimensions present significant obstacles. Machine learning has shown the potential to address these problems and drastically change the course of computer architecture design. First, traditional heuristic approaches to system design often struggle to cope with the increasing complexity of modern workloads and hardware platforms. Machine learning techniques offer a more flexible and adaptive alternative, capable of extracting insights from vast datasets to optimize architecture parameters. Moreover, machine learning enables a shift towards more holistic optimization strategies, considering multiple objectives simultaneously and accommodating diverse design constraints.Utilizing an accurate and low cost machine learning model to design effective computer systems is the goal of this dissertation. We showcase two machine learning models for two different purposes: In the first part, we present how we designed a highly accurate classifier with low overhead to partition the last level cache (LLC). LLC being a shared resource, is faced with performance and fairness challenges when running competing workloads with diverse behaviors. CASHT demonstrates my research focus on characterizing systems under contention and collecting data for the purpose of training machine learning models. These models, while accurate, have low hardware implementation overhead (considering the area, memory and latency) and are specifically designed to be integrated into current standard platforms.In the second part, we address the pressing issue of managing advanced hotspots on modern microprocessors, emphasizing its detrimental effect on performance, product reliability, and device lifetime which is only exacerbated as we decrease the transistor sizes. We introduce Boreas, an effective control system based on a low-cost regression model to prevent hotspots on single digit process nodes. Boreas demonstrates another dimension of my research on the use of machine learning for system design by training a model to assist the DVFS controller. Boreas outperforms existing thermal management techniques while remaining lightweight and well-suited for implementation in hardware.
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