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RRAM-CMOS Integrated Hardware for Efficient Learning and Inference At the Edge.
RRAM-CMOS Integrated Hardware for Efficient Learning and Inference At the Edge.
- 자료유형
- 학위논문
- Control Number
- 0016619603
- International Standard Book Number
- 9798351499727
- Dewey Decimal Classification Number
- 330
- Main Entry-Personal Name
- Li, Haitong.
- Publication, Distribution, etc. (Imprint
- [S.l.] : Stanford University., 2022
- Publication, Distribution, etc. (Imprint
- Ann Arbor : ProQuest Dissertations & Theses, 2022
- Physical Description
- 130 p.
- General Note
- Source: Dissertations Abstracts International, Volume: 84-04, Section: B.
- General Note
- Advisor: Mitra, Subhasish;Raina, Priyanka;Wong, H. S. Philip.
- Dissertation Note
- Thesis (Ph.D.)--Stanford University, 2022.
- Restrictions on Access Note
- This item must not be sold to any third party vendors.
- Subject Added Entry-Topical Term
- Sparsity.
- Subject Added Entry-Topical Term
- Space exploration.
- Subject Added Entry-Topical Term
- Technological change.
- Subject Added Entry-Topical Term
- Neural networks.
- Subject Added Entry-Topical Term
- Energy efficiency.
- Subject Added Entry-Topical Term
- Academic careers.
- Subject Added Entry-Topical Term
- Carbon footprint.
- Subject Added Entry-Topical Term
- 21st century.
- Subject Added Entry-Topical Term
- Transistors.
- Subject Added Entry-Topical Term
- Energy consumption.
- Subject Added Entry-Topical Term
- Energy.
- Added Entry-Corporate Name
- Stanford University.
- Host Item Entry
- Dissertations Abstracts International. 84-04B.
- Host Item Entry
- Dissertation Abstract International
- Electronic Location and Access
- 로그인을 한후 보실 수 있는 자료입니다.
- Control Number
- joongbu:625845
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