A 40-nm 118.44-TOPS/W Voltage-Sensing Compute-in-Memory RRAM Macro With Write Verification and Multi-Bit Encoding
Computing-in-memory (CIM) architectures have paved the way for energy-efficient artificial intelligence (AI) systems while outperforming von Neumann architectures. In particular, resistive RAM (RRAM)-based CIM has drawn attention due to high cell density, non-volatility, and compatibility with a CMO...
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Veröffentlicht in: | IEEE journal of solid-state circuits 2022-03, Vol.57 (3), p.845-857 |
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creator | Yoon, Jong-Hyeok Chang, Muya Khwa, Win-San Chih, Yu-Der Chang, Meng-Fan Raychowdhury, Arijit |
description | Computing-in-memory (CIM) architectures have paved the way for energy-efficient artificial intelligence (AI) systems while outperforming von Neumann architectures. In particular, resistive RAM (RRAM)-based CIM has drawn attention due to high cell density, non-volatility, and compatibility with a CMOS process. RRAM also exhibits the feasibility of high-capacity CIM with multi-bit encoding per cell exploiting an appropriate ON/OFF resistance ratio. However, the prior work regarding multi-level RRAM cells mainly focused on achieving higher bit resolution in write without consideration of CIM performance. Thus, the circuit solution to achieve multi-bit encoding per cell dedicated to RRAM-based CIM (RCIM) is of importance to support high-capacity AI systems with reliable CIM performance. This article presents a 256 \times 256 CIM multi-level RRAM macro featuring iterative write with verification to achieve reliable multi-bit encoding per cell and the voltage-sensing readout circuit to surmount the underlying logic ambiguity in RCIM architectures. In addition, we also demonstrate the key design space of a fabricated RRAM array in the write operation with extensive experiments. The test chip fabricated in a Taiwan Semiconductor Manufacturing Company (TSMC) 40-nm CMOS and RRAM process achieves a peak energy efficiency of 118.44 TOPS/W in the ternary-weight multiply-and-accumulate (MAC) operation and demonstrates the feasibility of multi-level RCIM with voltage-sensing RCIM. |
doi_str_mv | 10.1109/JSSC.2022.3141370 |
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In particular, resistive RAM (RRAM)-based CIM has drawn attention due to high cell density, non-volatility, and compatibility with a CMOS process. RRAM also exhibits the feasibility of high-capacity CIM with multi-bit encoding per cell exploiting an appropriate ON/OFF resistance ratio. However, the prior work regarding multi-level RRAM cells mainly focused on achieving higher bit resolution in write without consideration of CIM performance. Thus, the circuit solution to achieve multi-bit encoding per cell dedicated to RRAM-based CIM (RCIM) is of importance to support high-capacity AI systems with reliable CIM performance. This article presents a 256 <inline-formula> <tex-math notation="LaTeX">\times </tex-math></inline-formula> 256 CIM multi-level RRAM macro featuring iterative write with verification to achieve reliable multi-bit encoding per cell and the voltage-sensing readout circuit to surmount the underlying logic ambiguity in RCIM architectures. In addition, we also demonstrate the key design space of a fabricated RRAM array in the write operation with extensive experiments. The test chip fabricated in a Taiwan Semiconductor Manufacturing Company (TSMC) 40-nm CMOS and RRAM process achieves a peak energy efficiency of 118.44 TOPS/W in the ternary-weight multiply-and-accumulate (MAC) operation and demonstrates the feasibility of multi-level RCIM with voltage-sensing RCIM.</description><identifier>ISSN: 0018-9200</identifier><identifier>EISSN: 1558-173X</identifier><identifier>DOI: 10.1109/JSSC.2022.3141370</identifier><identifier>CODEN: IJSCBC</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>Artificial intelligence ; Circuits ; CMOS ; Common Information Model (computing) ; Computer architecture ; Computing-in-memory (CIM) ; convolutional neural network ; Electric potential ; Encoding ; Energy efficiency ; Feasibility ; Iterative methods ; Microprocessors ; multi-level cell ; multiply-and-accumulate (MAC) ; processing-in-memory ; Random access memory ; Resistance ; resistive RAM (RRAM) ; Verification ; Voltage ; write verification</subject><ispartof>IEEE journal of solid-state circuits, 2022-03, Vol.57 (3), p.845-857</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2022</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c293t-b55e603cfcc927194e3698878ac1aff37ef6b2b3982493b011531b556c59b3223</citedby><cites>FETCH-LOGICAL-c293t-b55e603cfcc927194e3698878ac1aff37ef6b2b3982493b011531b556c59b3223</cites><orcidid>0000-0002-3035-1106 ; 0000-0001-6905-6350 ; 0000-0001-7373-7028 ; 0000-0001-8391-0576</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/9689060$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,780,784,796,27922,27923,54756</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/9689060$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Yoon, Jong-Hyeok</creatorcontrib><creatorcontrib>Chang, Muya</creatorcontrib><creatorcontrib>Khwa, Win-San</creatorcontrib><creatorcontrib>Chih, Yu-Der</creatorcontrib><creatorcontrib>Chang, Meng-Fan</creatorcontrib><creatorcontrib>Raychowdhury, Arijit</creatorcontrib><title>A 40-nm 118.44-TOPS/W Voltage-Sensing Compute-in-Memory RRAM Macro With Write Verification and Multi-Bit Encoding</title><title>IEEE journal of solid-state circuits</title><addtitle>JSSC</addtitle><description>Computing-in-memory (CIM) architectures have paved the way for energy-efficient artificial intelligence (AI) systems while outperforming von Neumann architectures. In particular, resistive RAM (RRAM)-based CIM has drawn attention due to high cell density, non-volatility, and compatibility with a CMOS process. RRAM also exhibits the feasibility of high-capacity CIM with multi-bit encoding per cell exploiting an appropriate ON/OFF resistance ratio. However, the prior work regarding multi-level RRAM cells mainly focused on achieving higher bit resolution in write without consideration of CIM performance. Thus, the circuit solution to achieve multi-bit encoding per cell dedicated to RRAM-based CIM (RCIM) is of importance to support high-capacity AI systems with reliable CIM performance. This article presents a 256 <inline-formula> <tex-math notation="LaTeX">\times </tex-math></inline-formula> 256 CIM multi-level RRAM macro featuring iterative write with verification to achieve reliable multi-bit encoding per cell and the voltage-sensing readout circuit to surmount the underlying logic ambiguity in RCIM architectures. In addition, we also demonstrate the key design space of a fabricated RRAM array in the write operation with extensive experiments. The test chip fabricated in a Taiwan Semiconductor Manufacturing Company (TSMC) 40-nm CMOS and RRAM process achieves a peak energy efficiency of 118.44 TOPS/W in the ternary-weight multiply-and-accumulate (MAC) operation and demonstrates the feasibility of multi-level RCIM with voltage-sensing RCIM.</description><subject>Artificial intelligence</subject><subject>Circuits</subject><subject>CMOS</subject><subject>Common Information Model (computing)</subject><subject>Computer architecture</subject><subject>Computing-in-memory (CIM)</subject><subject>convolutional neural network</subject><subject>Electric potential</subject><subject>Encoding</subject><subject>Energy efficiency</subject><subject>Feasibility</subject><subject>Iterative methods</subject><subject>Microprocessors</subject><subject>multi-level cell</subject><subject>multiply-and-accumulate (MAC)</subject><subject>processing-in-memory</subject><subject>Random access memory</subject><subject>Resistance</subject><subject>resistive RAM (RRAM)</subject><subject>Verification</subject><subject>Voltage</subject><subject>write verification</subject><issn>0018-9200</issn><issn>1558-173X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNo9kM1uwjAQhK2qlUppH6DqxVLPDl7bSewjRfRPICqg0FuUGIcaQQKOc-DtawTqabXSzOzOh9Aj0AiAqt7nbDaIGGUs4iCAp_QKdSCOJYGU_1yjDqUgiWKU3qK7ptmEVQgJHXToY0FJtcMAMhKCzCdfs94SL-qtz9eGzEzV2GqNB_Vu33pDbEXGZle7I55O-2M8zrWr8dL6X7x01hu8MM6WVufe1hXOqxUet1tvyYv1eFjpehWy7tFNmW8b83CZXfT9OpwP3slo8vYx6I-IZop7UsSxSSjXpdaKpaCE4YmSMpW5hrwseWrKpGAFV5IJxQsKEHMIpkTHquCM8S56PufuXX1oTeOzTd26KpzMWMJBxUpQEVRwVoUiTeNMme2d3eXumAHNTmSzE9nsRDa7kA2ep7PHGmP-9SqRioaP_wASd3Ea</recordid><startdate>20220301</startdate><enddate>20220301</enddate><creator>Yoon, Jong-Hyeok</creator><creator>Chang, Muya</creator><creator>Khwa, Win-San</creator><creator>Chih, Yu-Der</creator><creator>Chang, Meng-Fan</creator><creator>Raychowdhury, Arijit</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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In addition, we also demonstrate the key design space of a fabricated RRAM array in the write operation with extensive experiments. The test chip fabricated in a Taiwan Semiconductor Manufacturing Company (TSMC) 40-nm CMOS and RRAM process achieves a peak energy efficiency of 118.44 TOPS/W in the ternary-weight multiply-and-accumulate (MAC) operation and demonstrates the feasibility of multi-level RCIM with voltage-sensing RCIM.</abstract><cop>New York</cop><pub>IEEE</pub><doi>10.1109/JSSC.2022.3141370</doi><tpages>13</tpages><orcidid>https://orcid.org/0000-0002-3035-1106</orcidid><orcidid>https://orcid.org/0000-0001-6905-6350</orcidid><orcidid>https://orcid.org/0000-0001-7373-7028</orcidid><orcidid>https://orcid.org/0000-0001-8391-0576</orcidid></addata></record> |
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subjects | Artificial intelligence Circuits CMOS Common Information Model (computing) Computer architecture Computing-in-memory (CIM) convolutional neural network Electric potential Encoding Energy efficiency Feasibility Iterative methods Microprocessors multi-level cell multiply-and-accumulate (MAC) processing-in-memory Random access memory Resistance resistive RAM (RRAM) Verification Voltage write verification |
title | A 40-nm 118.44-TOPS/W Voltage-Sensing Compute-in-Memory RRAM Macro With Write Verification and Multi-Bit Encoding |
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