{"id":"https://openalex.org/W3090581962","doi":"https://doi.org/10.1109/iscas45731.2020.9180701","title":"MC<sup>2</sup>RAM: Markov Chain Monte Carlo Sampling in SRAM for Fast Bayesian Inference","display_name":"MC<sup>2</sup>RAM: Markov Chain Monte Carlo Sampling in SRAM for Fast Bayesian Inference","publication_year":2020,"publication_date":"2020-09-29","ids":{"openalex":"https://openalex.org/W3090581962","doi":"https://doi.org/10.1109/iscas45731.2020.9180701","mag":"3090581962"},"language":"en","primary_location":{"id":"doi:10.1109/iscas45731.2020.9180701","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iscas45731.2020.9180701","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Symposium on Circuits and Systems (ISCAS)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5067601336","display_name":"Priyesh Shukla","orcid":"https://orcid.org/0000-0002-9868-8944"},"institutions":[{"id":"https://openalex.org/I39422238","display_name":"University of Illinois Chicago","ror":"https://ror.org/02mpq6x41","country_code":"US","type":"education","lineage":["https://openalex.org/I39422238"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Priyesh Shukla","raw_affiliation_strings":["University of Illinois at Chicago, IL, USA","University of Illinois at Chicago, Chicago, IL, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Illinois at Chicago, IL, USA","institution_ids":["https://openalex.org/I39422238"]},{"raw_affiliation_string":"University of Illinois at Chicago, Chicago, IL, USA","institution_ids":["https://openalex.org/I39422238"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5013872413","display_name":"Ahish Shylendra","orcid":"https://orcid.org/0000-0003-2388-0453"},"institutions":[{"id":"https://openalex.org/I39422238","display_name":"University of Illinois Chicago","ror":"https://ror.org/02mpq6x41","country_code":"US","type":"education","lineage":["https://openalex.org/I39422238"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ahish Shylendra","raw_affiliation_strings":["University of Illinois at Chicago, IL, USA","University of Illinois at Chicago, Chicago, IL, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Illinois at Chicago, IL, USA","institution_ids":["https://openalex.org/I39422238"]},{"raw_affiliation_string":"University of Illinois at Chicago, Chicago, IL, USA","institution_ids":["https://openalex.org/I39422238"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062751346","display_name":"Theja Tulabandhula","orcid":"https://orcid.org/0000-0001-9111-7519"},"institutions":[{"id":"https://openalex.org/I39422238","display_name":"University of Illinois Chicago","ror":"https://ror.org/02mpq6x41","country_code":"US","type":"education","lineage":["https://openalex.org/I39422238"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Theja Tulabandhula","raw_affiliation_strings":["University of Illinois at Chicago, IL, USA","University of Illinois at Chicago, Chicago, IL, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Illinois at Chicago, IL, USA","institution_ids":["https://openalex.org/I39422238"]},{"raw_affiliation_string":"University of Illinois at Chicago, Chicago, IL, USA","institution_ids":["https://openalex.org/I39422238"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5028132107","display_name":"Amit Ranjan Trivedi","orcid":"https://orcid.org/0000-0001-5436-7922"},"institutions":[{"id":"https://openalex.org/I39422238","display_name":"University of Illinois Chicago","ror":"https://ror.org/02mpq6x41","country_code":"US","type":"education","lineage":["https://openalex.org/I39422238"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Amit Ranjan Trivedi","raw_affiliation_strings":["The University of Illinois at Chicago, IL, USA","University of Illinois at Chicago, Chicago, IL, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The University of Illinois at Chicago, IL, USA","institution_ids":["https://openalex.org/I39422238"]},{"raw_affiliation_string":"University of Illinois at Chicago, Chicago, IL, USA","institution_ids":["https://openalex.org/I39422238"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I39422238"],"apc_list":null,"apc_paid":null,"fwci":2.3024,"has_fulltext":false,"cited_by_count":27,"citation_normalized_percentile":{"value":0.90806468,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.996999979019165,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.996999979019165,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.9904999732971191,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11269","display_name":"Algorithms and Data Compression","score":0.989799976348877,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/static-random-access-memory","display_name":"Static random-access memory","score":0.7999796867370605},{"id":"https://openalex.org/keywords/markov-chain-monte-carlo","display_name":"Markov chain Monte Carlo","score":0.7890521287918091},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.632994532585144},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6204516291618347},{"id":"https://openalex.org/keywords/monte-carlo-method","display_name":"Monte Carlo method","score":0.579634428024292},{"id":"https://openalex.org/keywords/cmos","display_name":"CMOS","score":0.5014946460723877},{"id":"https://openalex.org/keywords/importance-sampling","display_name":"Importance sampling","score":0.49806976318359375},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.49277523159980774},{"id":"https://openalex.org/keywords/slice-sampling","display_name":"Slice sampling","score":0.43543142080307007},{"id":"https://openalex.org/keywords/electronic-engineering","display_name":"Electronic engineering","score":0.38083744049072266},{"id":"https://openalex.org/keywords/parallel-computing","display_name":"Parallel computing","score":0.36485713720321655},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.2863233685493469},{"id":"https://openalex.org/keywords/computer-hardware","display_name":"Computer hardware","score":0.20141702890396118},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.19997483491897583},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.16124257445335388},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.15361088514328003},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.153192400932312},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.09144967794418335}],"concepts":[{"id":"https://openalex.org/C68043766","wikidata":"https://www.wikidata.org/wiki/Q267416","display_name":"Static random-access memory","level":2,"score":0.7999796867370605},{"id":"https://openalex.org/C111350023","wikidata":"https://www.wikidata.org/wiki/Q1191869","display_name":"Markov chain Monte Carlo","level":3,"score":0.7890521287918091},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.632994532585144},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6204516291618347},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.579634428024292},{"id":"https://openalex.org/C46362747","wikidata":"https://www.wikidata.org/wiki/Q173431","display_name":"CMOS","level":2,"score":0.5014946460723877},{"id":"https://openalex.org/C52740198","wikidata":"https://www.wikidata.org/wiki/Q1539564","display_name":"Importance sampling","level":3,"score":0.49806976318359375},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.49277523159980774},{"id":"https://openalex.org/C170593435","wikidata":"https://www.wikidata.org/wiki/Q4128565","display_name":"Slice sampling","level":4,"score":0.43543142080307007},{"id":"https://openalex.org/C24326235","wikidata":"https://www.wikidata.org/wiki/Q126095","display_name":"Electronic engineering","level":1,"score":0.38083744049072266},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.36485713720321655},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.2863233685493469},{"id":"https://openalex.org/C9390403","wikidata":"https://www.wikidata.org/wiki/Q3966","display_name":"Computer hardware","level":1,"score":0.20141702890396118},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.19997483491897583},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.16124257445335388},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.15361088514328003},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.153192400932312},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.09144967794418335},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iscas45731.2020.9180701","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iscas45731.2020.9180701","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Symposium on Circuits and Systems (ISCAS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W1567512734","https://openalex.org/W1999085092","https://openalex.org/W2010205010","https://openalex.org/W2033178790","https://openalex.org/W2080972498","https://openalex.org/W2108207895","https://openalex.org/W2135194391","https://openalex.org/W2156829028","https://openalex.org/W2236244207","https://openalex.org/W2591601611","https://openalex.org/W2604319603","https://openalex.org/W2785369415","https://openalex.org/W2928320209","https://openalex.org/W2951266961","https://openalex.org/W3101788429","https://openalex.org/W3123298421","https://openalex.org/W6764214684","https://openalex.org/W7075682605"],"related_works":["https://openalex.org/W3047994252","https://openalex.org/W2592308920","https://openalex.org/W4300460000","https://openalex.org/W2049791232","https://openalex.org/W2142819099","https://openalex.org/W3132384579","https://openalex.org/W2766819726","https://openalex.org/W769381313","https://openalex.org/W2736517747","https://openalex.org/W2951456667"],"abstract_inverted_index":{"This":[0],"work":[1],"discusses":[2],"the":[3,60,66,105],"implementation":[4,106],"of":[5,26,59,89],"Marko":[6],"Chain":[7],"Monte":[8],"Carlo":[9],"(MCMC)":[10],"sampling":[11,112,143],"from":[12],"an":[13,123],"arbitrary":[14],"Gaussian":[15],"mixture":[16,103],"model":[17],"(GMM)":[18],"within":[19,65,152],"SRAM.":[20],"We":[21,85],"show":[22],"a":[23,100],"novel":[24],"architecture":[25],"SRAM":[27,45,67,151],"by":[28,92],"embedding":[29],"it":[30],"with":[31],"random":[32],"number":[33],"generators":[34],"(RNGs),":[35],"digital-to-analog":[36],"converters":[37,41],"(DACs),":[38],"and":[39,68,114,145],"analog-to-digital":[40],"(ADCs)":[42],"so":[43],"that":[44],"arrays":[46],"can":[47,69,140],"be":[48,70],"used":[49],"for":[50,72,155],"high":[51,73,156],"performance":[52,157],"Metropolis-Hastings":[53],"(MH)":[54],"algorithm-based":[55],"MCMC":[56],"sampling.":[57,75,84,158],"Most":[58],"expensive":[61],"computations":[62],"are":[63],"performed":[64],"parallelized":[71],"speed":[74],"Our":[76,130],"iterative":[77],"compute":[78],"flow":[79],"minimizes":[80],"data":[81],"movement":[82],"during":[83],"characterize":[86],"power-performance":[87],"trade-off":[88],"our":[90],"design":[91],"simulating":[93],"on":[94,122,135],"45":[95],"nm":[96],"CMOS":[97],"technology.":[98],"For":[99],"two-dimensional,":[101],"two":[102],"GMM,":[104],"consumes":[107],"~":[108],"91\u03bcW":[109],"power":[110],"per":[111],"iteration":[113],"produces":[115],"500":[116],"samples":[117],"in":[118],"2000":[119],"clock":[120,128],"cycles":[121],"average":[124],"at":[125],"1":[126],"GHz":[127],"frequency.":[129],"study":[131],"highlights":[132],"interesting":[133],"insights":[134],"how":[136],"low-level":[137],"hardware":[138],"non-idealities":[139],"affect":[141],"high-level":[142],"characteristics,":[144],"recommends":[146],"ways":[147],"to":[148],"optimally":[149],"operate":[150],"area/power":[153],"constraints":[154]},"counts_by_year":[{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":7},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":5}],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-10-10T00:00:00"}