DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | Yoo, Hoi-Jun | - |
dc.contributor.advisor | 유회준 | - |
dc.contributor.author | Jo, Wooyoung | - |
dc.date.accessioned | 2023-06-26T19:34:25Z | - |
dc.date.available | 2023-06-26T19:34:25Z | - |
dc.date.issued | 2022 | - |
dc.identifier.uri | http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=1008370&flag=dissertation | en_US |
dc.identifier.uri | http://hdl.handle.net/10203/309978 | - |
dc.description | 학위논문(석사) - 한국과학기술원 : 전기및전자공학부, 2022.8,[iii, 21 p. :] | - |
dc.description.abstract | A Mixed-mode Computing-in memory (CIM) processor for the mixed-precision Deep Neural Network (DNN) processing is proposed. Due to the bit-serial processing for the multi-bit data, the previous CIM processors could not exploit the energy-efficient computation of mixed-precision DNNs. This paper proposes an energy-efficient mixed-mode CIM processor with two key features: 1) Mixed-Mode Mixed-precision CIM (M3-CIM) which achieves 55.46% energy efficiency improvement. 2) Digital-CIM for In-memory MAC for the increased throughput of M3-CIM. The proposed CIM processor was simulated in 28nm CMOS technology and occupies 1.96 mm2. It achieves a state-of-the-art energy efficiency of 161.6 TOPS/W with 72.8% accuracy at ImageNet (ResNet50). | - |
dc.language | eng | - |
dc.publisher | 한국과학기술원 | - |
dc.subject | Computing-in-Memory▼amixed-mode computing▼amixed-precision DNN processing▼aenergy-efficient▼aSRAM | - |
dc.subject | 메모리 내 컴퓨팅▼a혼성 모드 컴퓨팅▼a혼합 정밀도 DNN 연산▼a에너지 효율▼aSRAM | - |
dc.title | (A) mixed-mode computing-in-memory processor for energy-efficient mixed-precision deep neural networks | - |
dc.title.alternative | 에너지 효율적인 혼합 정밀도 심층 신경망 연산을 위한 혼성 모드 메모리 내 컴퓨팅 프로세서 | - |
dc.type | Thesis(Master) | - |
dc.identifier.CNRN | 325007 | - |
dc.description.department | 한국과학기술원 :전기및전자공학부, | - |
dc.contributor.alternativeauthor | 조우영 | - |
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