{"id":"https://openalex.org/W2520854767","doi":"https://doi.org/10.1145/2950067.2950100","title":"SSO-LSM: A Sparse and Self-Organizing architecture for Liquid State Machine based neural processors","display_name":"SSO-LSM: A Sparse and Self-Organizing architecture for Liquid State Machine based neural processors","publication_year":2016,"publication_date":"2016-07-18","ids":{"openalex":"https://openalex.org/W2520854767","doi":"https://doi.org/10.1145/2950067.2950100","mag":"2520854767"},"language":"en","primary_location":{"id":"mag:2520854767","is_oa":false,"landing_page_url":"http://ieeexplore.ieee.org/document/7568626/","pdf_url":null,"source":{"id":"https://openalex.org/S4306420180","display_name":"International Symposium on Nanoscale Architectures","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":"International Symposium on Nanoscale Architectures","raw_type":null},"type":"conference-paper","indexed_in":[],"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/A5024806967","display_name":"Yingyezhe Jin","orcid":null},"institutions":[{"id":"https://openalex.org/I91045830","display_name":"Texas A&M University","ror":"https://ror.org/01f5ytq51","country_code":"US","type":"education","lineage":["https://openalex.org/I91045830"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yingyezhe Jin","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Texas A&M University College Station, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Texas A&M University College Station, USA","institution_ids":["https://openalex.org/I91045830"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100345769","display_name":"Yu Liu","orcid":"https://orcid.org/0000-0002-4332-8124"},"institutions":[{"id":"https://openalex.org/I91045830","display_name":"Texas A&M University","ror":"https://ror.org/01f5ytq51","country_code":"US","type":"education","lineage":["https://openalex.org/I91045830"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yu Liu","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Texas A&M University College Station, USA"],"raw_orcid":"https://orcid.org/0000-0002-4332-8124","affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Texas A&M University College Station, USA","institution_ids":["https://openalex.org/I91045830"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100432640","display_name":"Peng Li","orcid":"https://orcid.org/0000-0001-8491-0199"},"institutions":[{"id":"https://openalex.org/I91045830","display_name":"Texas A&M University","ror":"https://ror.org/01f5ytq51","country_code":"US","type":"education","lineage":["https://openalex.org/I91045830"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Peng Li","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Texas A&M University College Station, USA"],"raw_orcid":"https://orcid.org/0000-0001-8491-0199","affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Texas A&M University College Station, USA","institution_ids":["https://openalex.org/I91045830"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I91045830"],"apc_list":null,"apc_paid":null,"fwci":2.7178,"has_fulltext":false,"cited_by_count":24,"citation_normalized_percentile":{"value":0.92959082,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"55","last_page":"60"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12611","display_name":"Neural Networks and Reservoir Computing","score":0.9998999834060669,"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/T12611","display_name":"Neural Networks and Reservoir Computing","score":0.9998999834060669,"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/T10502","display_name":"Advanced Memory and Neural Computing","score":0.9994999766349792,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12676","display_name":"Machine Learning and ELM","score":0.9851999878883362,"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/computer-science","display_name":"Computer science","score":0.7636144161224365},{"id":"https://openalex.org/keywords/control-reconfiguration","display_name":"Control reconfiguration","score":0.6879529356956482},{"id":"https://openalex.org/keywords/field-programmable-gate-array","display_name":"Field-programmable gate array","score":0.6615281701087952},{"id":"https://openalex.org/keywords/reservoir-computing","display_name":"Reservoir computing","score":0.6287636756896973},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6258540153503418},{"id":"https://openalex.org/keywords/overhead","display_name":"Overhead (engineering)","score":0.5560850501060486},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.4991731643676758},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.447113960981369},{"id":"https://openalex.org/keywords/computer-architecture","display_name":"Computer architecture","score":0.44608959555625916},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.4394172728061676},{"id":"https://openalex.org/keywords/parallel-computing","display_name":"Parallel computing","score":0.40625569224357605},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3816871643066406},{"id":"https://openalex.org/keywords/embedded-system","display_name":"Embedded system","score":0.367745578289032},{"id":"https://openalex.org/keywords/computer-engineering","display_name":"Computer engineering","score":0.3544287085533142}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7636144161224365},{"id":"https://openalex.org/C119701452","wikidata":"https://www.wikidata.org/wiki/Q5165881","display_name":"Control reconfiguration","level":2,"score":0.6879529356956482},{"id":"https://openalex.org/C42935608","wikidata":"https://www.wikidata.org/wiki/Q190411","display_name":"Field-programmable gate array","level":2,"score":0.6615281701087952},{"id":"https://openalex.org/C135796866","wikidata":"https://www.wikidata.org/wiki/Q7315328","display_name":"Reservoir computing","level":4,"score":0.6287636756896973},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6258540153503418},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.5560850501060486},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.4991731643676758},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.447113960981369},{"id":"https://openalex.org/C118524514","wikidata":"https://www.wikidata.org/wiki/Q173212","display_name":"Computer architecture","level":1,"score":0.44608959555625916},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.4394172728061676},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.40625569224357605},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3816871643066406},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.367745578289032},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.3544287085533142},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"mag:2520854767","is_oa":false,"landing_page_url":"http://ieeexplore.ieee.org/document/7568626/","pdf_url":null,"source":{"id":"https://openalex.org/S4306420180","display_name":"International Symposium on Nanoscale Architectures","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":"International Symposium on Nanoscale Architectures","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/7","score":0.8700000047683716,"display_name":"Affordable and clean energy"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W1480485976","https://openalex.org/W1519227916","https://openalex.org/W1578457181","https://openalex.org/W1969104262","https://openalex.org/W1979854415","https://openalex.org/W1986964674","https://openalex.org/W2025920171","https://openalex.org/W2076015009","https://openalex.org/W2096473662","https://openalex.org/W2103179919","https://openalex.org/W2105580042","https://openalex.org/W2117578469","https://openalex.org/W2125486261","https://openalex.org/W2151542182","https://openalex.org/W2171865010","https://openalex.org/W2185878459","https://openalex.org/W2508829650","https://openalex.org/W3022542908","https://openalex.org/W3099280452"],"related_works":["https://openalex.org/W2103179919","https://openalex.org/W2076015009","https://openalex.org/W2556009568","https://openalex.org/W2185878459","https://openalex.org/W2783304281","https://openalex.org/W2742696816","https://openalex.org/W2783525259","https://openalex.org/W2171865010","https://openalex.org/W2105580042","https://openalex.org/W1986964674","https://openalex.org/W1486852018","https://openalex.org/W1480485976","https://openalex.org/W2519601090","https://openalex.org/W3174494699","https://openalex.org/W2802512292","https://openalex.org/W2968715446","https://openalex.org/W2508829650","https://openalex.org/W1482483597","https://openalex.org/W1982456208","https://openalex.org/W345956458"],"abstract_inverted_index":{"The":[0,25,109],"Liquid":[1],"State":[2],"Machine":[3],"(LSM)":[4],"is":[5,92],"a":[6,30,36,41,66,75,131,140,180,205,215],"powerful":[7],"recurrent":[8,33,133],"spiking":[9],"neural":[10,23,62],"network":[11],"model":[12,28],"that":[13,127,184],"provides":[14],"an":[15],"appealing":[16],"paradigm":[17],"of":[18,162],"computation":[19],"for":[20,83,144],"realizing":[21],"brain-inspired":[22],"processors.":[24],"conventional":[26],"LSM":[27,71,207],"incorporates":[29,139],"random":[31],"fixed":[32],"reservoir":[34,53,86,126,152],"as":[35,179],"general":[37],"pre-processing":[38],"kernel":[39],"and":[40,69],"trainable":[42],"readout":[43,155],"layer":[44,156],"which":[45],"extracts":[46],"the":[47,52,96,125,136,146,151,154,159,166,169,175,185,189],"firing":[48,163],"activities":[49,164],"embedded":[50],"in":[51,101,124,165],"to":[54,94,130,153,204],"facilitate":[55],"pattern":[56],"recognition.":[57],"To":[58],"realize":[59],"adaptive":[60],"LSM-based":[61],"processors,":[63],"we":[64,182],"propose":[65],"novel":[67],"Sparse":[68],"Self-Organizing":[70],"(SSO-LSM)":[72],"architecture":[73,138,187],"with":[74,104,209],"low-overhead":[76],"hardware-friendly":[77],"Spike-Timing":[78],"Dependent":[79],"Plasticity":[80],"(STDP)":[81],"mechanism":[82],"efficient":[84],"on-chip":[85],"tuning.":[87],"A":[88],"data-driven":[89],"optimization":[90],"flow":[91],"presented":[93],"implement":[95],"targeted":[97],"STDP":[98,111],"rule":[99,112],"efficiently":[100],"digital":[102],"logic":[103],"extremely":[105],"low":[106],"bit":[107],"resolutions.":[108],"proposed":[110],"not":[113],"only":[114],"boosts":[115,188],"learning":[116,191],"performance,":[117],"but":[118],"also":[119],"induces":[120],"desirable":[121],"self-organizing":[122],"behaviors":[123],"naturally":[128],"lead":[129],"sparser":[132],"network.":[134],"Furthermore,":[135],"SSO-LSM":[137,186],"runtime":[141],"reconfiguration":[142],"scheme":[143],"sparsifying":[145],"synaptic":[147],"connections":[148],"projected":[149],"from":[150,174],"based":[157],"upon":[158],"monitored":[160],"variances":[161],"reservoir.":[167],"Using":[168],"spoken":[170],"English":[171],"letters":[172],"adopted":[173],"TI46":[176],"speech":[177],"corpus":[178],"benchmark,":[181],"demonstrate":[183],"average":[190],"performance":[192],"rather":[193],"significantly":[194],"by":[195,201],"2.0%":[196],"while":[197],"reducing":[198],"energy":[199],"dissipation":[200],"25%":[202],"compared":[203],"baseline":[206],"design":[208],"little":[210],"extra":[211],"hardware":[212],"overhead":[213],"on":[214],"Xilinx":[216],"Virtex-6":[217],"FPGA.":[218]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":3},{"year":2018,"cited_by_count":7},{"year":2017,"cited_by_count":4}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
