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dc.contributor.author Yoon, Jong-Hyeok -
dc.contributor.author Chang, Muya -
dc.contributor.author Khwa, Win-San -
dc.contributor.author Chih, Yu-Der -
dc.contributor.author Chang, Meng-Fan -
dc.contributor.author Raychowdhury, Arijit -
dc.date.accessioned 2023-12-26T18:44:31Z -
dc.date.available 2023-12-26T18:44:31Z -
dc.date.created 2021-11-27 -
dc.date.issued 2021-04-29 -
dc.identifier.isbn 9781728175812 -
dc.identifier.issn 2152-3630 -
dc.identifier.uri http://hdl.handle.net/20.500.11750/46936 -
dc.description.abstract RRAM is a promising candidate for compute-in-memory (CIM) applications owing to its natural multiply-and-accumulate (MAC)-supporting structure, high bit-density, non-volatility, and a monolithic CMOS and RRAM process. In particular, multi-bit encoding in RRAM cells helps support advanced applications such as AI with higher MAC throughput and bit-density. Notwithstanding prior efforts into commercializing RRAM technology, underlying challenges hinder the wide usage of RRAM [1]. As a circuit-domain approach to address the challenges, this paper presents a 101.4Kb ternary-weight RRAM macro with 256x256 cells supporting: (1) CIM for ternary weight networks by employing voltage-based read (RD) with active feedback surmounting a low resistance ratio (R-ratio) between the high resistance state (HRS) and the low resistance state (LRS) in high-endurance RRAM, and (2) iterative write with verification (IWR) to facilitate a reliable multi-bit encoding under a narrow margin. Compared to [2] supporting CIM with binary RRAM cells, this work provides 38.44x (=33x3/23x3) flexibility on 3x3 filters in convolutional neural networks (CNNs), and 1.585x bit density improvement, thereby enabling advanced CIM applications with ternary weight networks. © 2021 IEEE. -
dc.language English -
dc.publisher IEEE Solid-State Circuits Society -
dc.title A 40nm 100Kb 118.44TOPS/W Ternary-weight Computein-Memory RRAM Macro with Voltage-sensing Read and Write Verification for reliable multi-bit RRAM operation -
dc.type Conference Paper -
dc.identifier.doi 10.1109/CICC51472.2021.9431412 -
dc.identifier.scopusid 2-s2.0-85107200391 -
dc.identifier.bibliographicCitation IEEE Custom Integrated Circuits Conference (CICC 2021), pp.152 - 153 -
dc.identifier.url https://cicc2021.exordo.com/programme/presentation/31 -
dc.citation.conferencePlace US -
dc.citation.conferencePlace Austin -
dc.citation.endPage 153 -
dc.citation.startPage 152 -
dc.citation.title IEEE Custom Integrated Circuits Conference (CICC 2021) -
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Department of Electrical Engineering and Computer Science Intelligent Integrated Circuits and Systems Lab 2. Conference Papers

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