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Design Methodology and Electronic Design Automation Techniques for Heterogeneous 3D Machine Learning Accelerators
Design Methodology and Electronic Design Automation Techniques for Heterogeneous 3D Machine Learning Accelerators
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20260202105546
- ISBN
- 9798265405456
- DDC
- 629.4
- 서명/저자
- Design Methodology and Electronic Design Automation Techniques for Heterogeneous 3D Machine Learning Accelerators
- 발행사항
- [Sl] : Georgia Institute of Technology, 2023
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2023
- 형태사항
- 133 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 87-05, Section: B.
- 주기사항
- Advisor: Lim, Sung Kyu.
- 학위논문주기
- Thesis (Ph.D.)--Georgia Institute of Technology, 2023.
- 초록/해제
- 요약The primary goal of this thesis is to present comprehensive design methodologies and electronic design automation (EDA) techniques aimed at enhancing the efficiency of 3D machine learning accelerator designs. This research extensively investigates the challenges associated with scaling near-memory and in-memory compute accelerators in the X/Y dimensions and proposes optimized scaling solutions by leveraging heterogeneous 3D integration.While numerous 3D physical design EDA methodologies exist, they often involve a multitude of parameters that require careful tuning based on the specific design in question. To address this, the thesis examines the impact of these parameters on various design types using cutting-edge machine learning-based parameter autotuning tools. It subsequently recommends the most effective parameter tuning techniques depending on the particular 3D EDA approach being employed.Using the EDA tools optimized through machine learning, the thesis delves into the advantages of heterogeneous 3D implementation for near-memory and in-memory compute ML accelerators at various technology nodes. Heterogeneous 3D integration delivers substantial improvements in terms of Power-Performance-Area (PPA), throughput, energy efficiency, and area efficiency when compared to their 2D counterparts.Finally, the thesis introduces a straightforward yet highly efficient machine learning framework for exploring the design space of heterogeneous 3D accelerators. This framework assists designers in selecting the optimal accelerator architecture tailored to the requirements of their target applications.
- 일반주제명
- Space exploration
- 일반주제명
- Transistors
- 일반주제명
- Aerospace engineering
- 일반주제명
- Electrical engineering
- 기본자료저록
- Dissertations Abstracts International. 87-05B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■007cr#unu||||||||
■020 ▼a9798265405456
■035 ▼a(MiAaPQ)AAI32315570
■035 ▼a(MiAaPQ)GeorgiaTech73118
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a629.4
■1001 ▼aMurali, Gauthaman.
■24510▼aDesign Methodology and Electronic Design Automation Techniques for Heterogeneous 3D Machine Learning Accelerators
■260 ▼a[Sl]▼bGeorgia Institute of Technology▼c2023
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2023
■300 ▼a133 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 87-05, Section: B.
■500 ▼aAdvisor: Lim, Sung Kyu.
■5021 ▼aThesis (Ph.D.)--Georgia Institute of Technology, 2023.
■520 ▼aThe primary goal of this thesis is to present comprehensive design methodologies and electronic design automation (EDA) techniques aimed at enhancing the efficiency of 3D machine learning accelerator designs. This research extensively investigates the challenges associated with scaling near-memory and in-memory compute accelerators in the X/Y dimensions and proposes optimized scaling solutions by leveraging heterogeneous 3D integration.While numerous 3D physical design EDA methodologies exist, they often involve a multitude of parameters that require careful tuning based on the specific design in question. To address this, the thesis examines the impact of these parameters on various design types using cutting-edge machine learning-based parameter autotuning tools. It subsequently recommends the most effective parameter tuning techniques depending on the particular 3D EDA approach being employed.Using the EDA tools optimized through machine learning, the thesis delves into the advantages of heterogeneous 3D implementation for near-memory and in-memory compute ML accelerators at various technology nodes. Heterogeneous 3D integration delivers substantial improvements in terms of Power-Performance-Area (PPA), throughput, energy efficiency, and area efficiency when compared to their 2D counterparts.Finally, the thesis introduces a straightforward yet highly efficient machine learning framework for exploring the design space of heterogeneous 3D accelerators. This framework assists designers in selecting the optimal accelerator architecture tailored to the requirements of their target applications.
■590 ▼aSchool code: 0078.
■650 4▼aSpace exploration
■650 4▼aTransistors
■650 4▼aAerospace engineering
■650 4▼aElectrical engineering
■690 ▼a0538
■690 ▼a0800
■690 ▼a0544
■71020▼aGeorgia Institute of Technology.
■7730 ▼tDissertations Abstracts International▼g87-05B.
■790 ▼a0078
■791 ▼aPh.D.
■792 ▼a2023
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17360552▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
