東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁 到查詢結果 [ null ]

切換: 標籤 | MARC模式 | ISBD

Cost Effective Machine Learning for ...

Amiraski, Maziar.

FindBook

Google Book

Amazon

博客來

Cost Effective Machine Learning for Computer Architecture Design.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Cost Effective Machine Learning for Computer Architecture Design./
作者:	Amiraski, Maziar.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2024,
面頁冊數:	110 p.
附註:	Source: Dissertations Abstracts International, Volume: 85-11, Section: A.
Contained By:	Dissertations Abstracts International85-11A.
標題:	Computer engineering. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31293336
ISBN:	9798382737515

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

Cost Effective Machine Learning for Computer Architecture Design. - Ann Arbor : ProQuest Dissertations & Theses, 2024 - 110 p.

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

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

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.

ISBN: 9798382737515Subjects--Topical Terms:

621879
Computer engineering.
Subjects--Index Terms:

Machine learning

Cost Effective Machine Learning for Computer Architecture Design.
LDR:03746nmm a2200409 4500 001 2401483
005 20241022112627.5
006 m o d
007 cr#unu||||||||
008 251215s2024 ||||||||||||||||| ||eng d
020 $a 9798382737515
035 $a (MiAaPQ)AAI31293336
035 $a AAI31293336
035 $a 2401483
040 $a MiAaPQ $c MiAaPQ
100 1 $a Amiraski, Maziar. $3 3771575
245 1 0 $a Cost Effective Machine Learning for Computer Architecture Design.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2024
300 $a 110 p.
500 $a Source: Dissertations Abstracts International, Volume: 85-11, Section: A.
500 $a Advisor: Hempstead, Mark.
502 $a Thesis (Ph.D.)--Tufts University, 2024.
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.
590 $a School code: 0234.
650 4 $a Computer engineering. $3 621879
650 4 $a Architectural engineering. $3 3174102
650 4 $a Landscape architecture. $3 541842
653 $a Machine learning
653 $a Last level cache
653 $a Computer architecture design
653 $a Hardware platforms
653 $a Microprocessors
690 $a 0464
690 $a 0800
690 $a 0390
690 $a 0462
710 2 $a Tufts University. $b Electrical Engineering. $3 1030762
773 0 $t Dissertations Abstracts International $g 85-11A.
790 $a 0234
791 $a Ph.D.
792 $a 2024
793 $a English
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31293336