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...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:IEEE journal of solid-state circuits 2022-03, Vol.57 (3), p.845-857
Hauptverfasser: Yoon, Jong-Hyeok, Chang, Muya, Khwa, Win-San, Chih, Yu-Der, Chang, Meng-Fan, Raychowdhury, Arijit
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext bestellen
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page 857
container_issue 3
container_start_page 845
container_title IEEE journal of solid-state circuits
container_volume 57
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
format Article
fullrecord <record><control><sourceid>proquest_RIE</sourceid><recordid>TN_cdi_ieee_primary_9689060</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>9689060</ieee_id><sourcerecordid>2631959404</sourcerecordid><originalsourceid>FETCH-LOGICAL-c293t-b55e603cfcc927194e3698878ac1aff37ef6b2b3982493b011531b556c59b3223</originalsourceid><addsrcrecordid>eNo9kM1uwjAQhK2qlUppH6DqxVLPDl7bSewjRfRPICqg0FuUGIcaQQKOc-DtawTqabXSzOzOh9Aj0AiAqt7nbDaIGGUs4iCAp_QKdSCOJYGU_1yjDqUgiWKU3qK7ptmEVQgJHXToY0FJtcMAMhKCzCdfs94SL-qtz9eGzEzV2GqNB_Vu33pDbEXGZle7I55O-2M8zrWr8dL6X7x01hu8MM6WVufe1hXOqxUet1tvyYv1eFjpehWy7tFNmW8b83CZXfT9OpwP3slo8vYx6I-IZop7UsSxSSjXpdaKpaCE4YmSMpW5hrwseWrKpGAFV5IJxQsKEHMIpkTHquCM8S56PufuXX1oTeOzTd26KpzMWMJBxUpQEVRwVoUiTeNMme2d3eXumAHNTmSzE9nsRDa7kA2ep7PHGmP-9SqRioaP_wASd3Ea</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2631959404</pqid></control><display><type>article</type><title>A 40-nm 118.44-TOPS/W Voltage-Sensing Compute-in-Memory RRAM Macro With Write Verification and Multi-Bit Encoding</title><source>IEEE Electronic Library (IEL)</source><creator>Yoon, Jong-Hyeok ; Chang, Muya ; Khwa, Win-San ; Chih, Yu-Der ; Chang, Meng-Fan ; Raychowdhury, Arijit</creator><creatorcontrib>Yoon, Jong-Hyeok ; Chang, Muya ; Khwa, Win-San ; Chih, Yu-Der ; Chang, Meng-Fan ; Raychowdhury, Arijit</creatorcontrib><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 &lt;inline-formula&gt; &lt;tex-math notation="LaTeX"&gt;\times &lt;/tex-math&gt;&lt;/inline-formula&gt; 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 &lt;inline-formula&gt; &lt;tex-math notation="LaTeX"&gt;\times &lt;/tex-math&gt;&lt;/inline-formula&gt; 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. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SP</scope><scope>8FD</scope><scope>L7M</scope><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></search><sort><creationdate>20220301</creationdate><title>A 40-nm 118.44-TOPS/W Voltage-Sensing Compute-in-Memory RRAM Macro With Write Verification and Multi-Bit Encoding</title><author>Yoon, Jong-Hyeok ; Chang, Muya ; Khwa, Win-San ; Chih, Yu-Der ; Chang, Meng-Fan ; Raychowdhury, Arijit</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c293t-b55e603cfcc927194e3698878ac1aff37ef6b2b3982493b011531b556c59b3223</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Artificial intelligence</topic><topic>Circuits</topic><topic>CMOS</topic><topic>Common Information Model (computing)</topic><topic>Computer architecture</topic><topic>Computing-in-memory (CIM)</topic><topic>convolutional neural network</topic><topic>Electric potential</topic><topic>Encoding</topic><topic>Energy efficiency</topic><topic>Feasibility</topic><topic>Iterative methods</topic><topic>Microprocessors</topic><topic>multi-level cell</topic><topic>multiply-and-accumulate (MAC)</topic><topic>processing-in-memory</topic><topic>Random access memory</topic><topic>Resistance</topic><topic>resistive RAM (RRAM)</topic><topic>Verification</topic><topic>Voltage</topic><topic>write verification</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><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><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>CrossRef</collection><collection>Electronics &amp; Communications Abstracts</collection><collection>Technology Research Database</collection><collection>Advanced Technologies Database with Aerospace</collection><jtitle>IEEE journal of solid-state circuits</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Yoon, Jong-Hyeok</au><au>Chang, Muya</au><au>Khwa, Win-San</au><au>Chih, Yu-Der</au><au>Chang, Meng-Fan</au><au>Raychowdhury, Arijit</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A 40-nm 118.44-TOPS/W Voltage-Sensing Compute-in-Memory RRAM Macro With Write Verification and Multi-Bit Encoding</atitle><jtitle>IEEE journal of solid-state circuits</jtitle><stitle>JSSC</stitle><date>2022-03-01</date><risdate>2022</risdate><volume>57</volume><issue>3</issue><spage>845</spage><epage>857</epage><pages>845-857</pages><issn>0018-9200</issn><eissn>1558-173X</eissn><coden>IJSCBC</coden><abstract>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 &lt;inline-formula&gt; &lt;tex-math notation="LaTeX"&gt;\times &lt;/tex-math&gt;&lt;/inline-formula&gt; 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.</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>
fulltext fulltext_linktorsrc
identifier ISSN: 0018-9200
ispartof IEEE journal of solid-state circuits, 2022-03, Vol.57 (3), p.845-857
issn 0018-9200
1558-173X
language eng
recordid cdi_ieee_primary_9689060
source IEEE Electronic Library (IEL)
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
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-10T07%3A55%3A59IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_RIE&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=A%2040-nm%20118.44-TOPS/W%20Voltage-Sensing%20Compute-in-Memory%20RRAM%20Macro%20With%20Write%20Verification%20and%20Multi-Bit%20Encoding&rft.jtitle=IEEE%20journal%20of%20solid-state%20circuits&rft.au=Yoon,%20Jong-Hyeok&rft.date=2022-03-01&rft.volume=57&rft.issue=3&rft.spage=845&rft.epage=857&rft.pages=845-857&rft.issn=0018-9200&rft.eissn=1558-173X&rft.coden=IJSCBC&rft_id=info:doi/10.1109/JSSC.2022.3141370&rft_dat=%3Cproquest_RIE%3E2631959404%3C/proquest_RIE%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2631959404&rft_id=info:pmid/&rft_ieee_id=9689060&rfr_iscdi=true