{"id":"https://openalex.org/W2787190482","doi":"https://doi.org/10.1109/ssci.2017.8285202","title":"Layer-wise synapse optimization for implementing neural networks on general neuromorphic architectures","display_name":"Layer-wise synapse optimization for implementing neural networks on general neuromorphic architectures","publication_year":2017,"publication_date":"2017-11-01","ids":{"openalex":"https://openalex.org/W2787190482","doi":"https://doi.org/10.1109/ssci.2017.8285202","mag":"2787190482"},"language":"en","primary_location":{"id":"doi:10.1109/ssci.2017.8285202","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ssci.2017.8285202","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE Symposium Series on Computational Intelligence (SSCI)","raw_type":"proceedings-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1802.06920","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"John Mern","orcid":null},"institutions":[{"id":"https://openalex.org/I97018004","display_name":"Stanford University","ror":"https://ror.org/00f54p054","country_code":"US","type":"education","lineage":["https://openalex.org/I97018004"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"John Mern","raw_affiliation_strings":["Graduate Student in the Stanford Intelligent Systems Laboratory, Stanford University"],"affiliations":[{"raw_affiliation_string":"Graduate Student in the Stanford Intelligent Systems Laboratory, Stanford University","institution_ids":["https://openalex.org/I97018004"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Jayesh K. Gupta","orcid":null},"institutions":[{"id":"https://openalex.org/I97018004","display_name":"Stanford University","ror":"https://ror.org/00f54p054","country_code":"US","type":"education","lineage":["https://openalex.org/I97018004"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jayesh K. Gupta","raw_affiliation_strings":["Graduate Student in the Stanford Intelligent Systems Laboratory, Stanford University"],"affiliations":[{"raw_affiliation_string":"Graduate Student in the Stanford Intelligent Systems Laboratory, Stanford University","institution_ids":["https://openalex.org/I97018004"]}]},{"author_position":"last","author":{"id":null,"display_name":"Mykel J. Kochenderfer","orcid":null},"institutions":[{"id":"https://openalex.org/I97018004","display_name":"Stanford University","ror":"https://ror.org/00f54p054","country_code":"US","type":"education","lineage":["https://openalex.org/I97018004"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mykel J. Kochenderfer","raw_affiliation_strings":["Department of Aeronautics and Astronautics, Stanford University"],"affiliations":[{"raw_affiliation_string":"Department of Aeronautics and Astronautics, Stanford University","institution_ids":["https://openalex.org/I97018004"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I97018004"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.19742433,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"abs 1510 149","issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10502","display_name":"Advanced Memory and Neural Computing","score":1.0,"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"}},"topics":[{"id":"https://openalex.org/T10502","display_name":"Advanced Memory and Neural Computing","score":1.0,"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/T12808","display_name":"Ferroelectric and Negative Capacitance Devices","score":0.9984999895095825,"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/T11992","display_name":"CCD and CMOS Imaging Sensors","score":0.9904999732971191,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/neuromorphic-engineering","display_name":"Neuromorphic engineering","score":0.911300003528595},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5879999995231628},{"id":"https://openalex.org/keywords/pruning","display_name":"Pruning","score":0.5745999813079834},{"id":"https://openalex.org/keywords/solver","display_name":"Solver","score":0.5676000118255615},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5559999942779541},{"id":"https://openalex.org/keywords/spiking-neural-network","display_name":"Spiking neural network","score":0.552299976348877},{"id":"https://openalex.org/keywords/bottleneck","display_name":"Bottleneck","score":0.4885999858379364},{"id":"https://openalex.org/keywords/von-neumann-architecture","display_name":"Von Neumann architecture","score":0.46720001101493835},{"id":"https://openalex.org/keywords/node","display_name":"Node (physics)","score":0.44999998807907104}],"concepts":[{"id":"https://openalex.org/C151927369","wikidata":"https://www.wikidata.org/wiki/Q1981312","display_name":"Neuromorphic engineering","level":3,"score":0.911300003528595},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7699999809265137},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5879999995231628},{"id":"https://openalex.org/C108010975","wikidata":"https://www.wikidata.org/wiki/Q500094","display_name":"Pruning","level":2,"score":0.5745999813079834},{"id":"https://openalex.org/C2778770139","wikidata":"https://www.wikidata.org/wiki/Q1966904","display_name":"Solver","level":2,"score":0.5676000118255615},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5559999942779541},{"id":"https://openalex.org/C11731999","wikidata":"https://www.wikidata.org/wiki/Q9067355","display_name":"Spiking neural network","level":3,"score":0.552299976348877},{"id":"https://openalex.org/C2780513914","wikidata":"https://www.wikidata.org/wiki/Q18210350","display_name":"Bottleneck","level":2,"score":0.4885999858379364},{"id":"https://openalex.org/C80469333","wikidata":"https://www.wikidata.org/wiki/Q189088","display_name":"Von Neumann architecture","level":2,"score":0.46720001101493835},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.44999998807907104},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4336000084877014},{"id":"https://openalex.org/C38365724","wikidata":"https://www.wikidata.org/wiki/Q4677469","display_name":"Activation function","level":3,"score":0.4147000014781952},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.40860000252723694},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.4077000021934509},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.3790000081062317},{"id":"https://openalex.org/C149810388","wikidata":"https://www.wikidata.org/wiki/Q5374873","display_name":"Emulation","level":2,"score":0.3732999861240387},{"id":"https://openalex.org/C2780586882","wikidata":"https://www.wikidata.org/wiki/Q7520643","display_name":"Simple (philosophy)","level":2,"score":0.36340001225471497},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.3422999978065491},{"id":"https://openalex.org/C118524514","wikidata":"https://www.wikidata.org/wiki/Q173212","display_name":"Computer architecture","level":1,"score":0.3327000141143799},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.3188000023365021},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.3124000132083893},{"id":"https://openalex.org/C2780165032","wikidata":"https://www.wikidata.org/wiki/Q16869822","display_name":"Energy consumption","level":2,"score":0.2980000078678131},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2935999929904938},{"id":"https://openalex.org/C2777735758","wikidata":"https://www.wikidata.org/wiki/Q817765","display_name":"Path (computing)","level":2,"score":0.288100004196167},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.28299999237060547},{"id":"https://openalex.org/C2742236","wikidata":"https://www.wikidata.org/wiki/Q924713","display_name":"Efficient energy use","level":2,"score":0.27970001101493835},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.2784000039100647},{"id":"https://openalex.org/C111696304","wikidata":"https://www.wikidata.org/wiki/Q2303697","display_name":"Sorting","level":2,"score":0.27559998631477356},{"id":"https://openalex.org/C77390884","wikidata":"https://www.wikidata.org/wiki/Q217302","display_name":"Application-specific integrated circuit","level":2,"score":0.2727000117301941},{"id":"https://openalex.org/C2779227376","wikidata":"https://www.wikidata.org/wiki/Q6505497","display_name":"Layer (electronics)","level":2,"score":0.2703999876976013}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/ssci.2017.8285202","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ssci.2017.8285202","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE Symposium Series on Computational Intelligence (SSCI)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:1802.06920","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1802.06920","pdf_url":"https://arxiv.org/pdf/1802.06920","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1802.06920","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1802.06920","pdf_url":"https://arxiv.org/pdf/1802.06920","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W1645800954","https://openalex.org/W1695065985","https://openalex.org/W2006370340","https://openalex.org/W2019980601","https://openalex.org/W2065125569","https://openalex.org/W2084910356","https://openalex.org/W2112796928","https://openalex.org/W2130360162","https://openalex.org/W2138913040","https://openalex.org/W2314470091","https://openalex.org/W2541249978","https://openalex.org/W2595593680","https://openalex.org/W2963157821","https://openalex.org/W2963743287","https://openalex.org/W6631190155","https://openalex.org/W6638018090","https://openalex.org/W6677580257","https://openalex.org/W6729407136","https://openalex.org/W6730823560"],"related_works":[],"abstract_inverted_index":{"Deep":[0],"artificial":[1],"neural":[2],"networks":[3,178,193],"(ANNs)":[4],"can":[5,118],"represent":[6],"a":[7,22,67,84,102,165],"wide":[8],"range":[9],"of":[10,94,105],"complex":[11],"functions.":[12],"Implementing":[13],"ANNs":[14,46],"in":[15,56,197,202],"Von":[16],"Neumann":[17],"computing":[18],"systems,":[19],"though,":[20],"incurs":[21],"high":[23],"energy":[24,43],"cost":[25],"due":[26],"to":[27,41,58,69,78,136,141,159],"the":[28,75,145,176,182,189],"bottleneck":[29],"created":[30],"between":[31],"CPU":[32],"and":[33,109,185],"memory.":[34],"Implementation":[35],"on":[36,61,188],"neuromorphic":[37,62],"systems":[38],"may":[39,97],"help":[40],"reduce":[42],"demand.":[44],"Conventional":[45],"must":[47],"be":[48,59,99,142],"converted":[49],"into":[50],"equivalent":[51],"Spiking":[52],"Neural":[53],"Networks":[54],"(SNNs)":[55],"order":[57],"deployed":[60],"chips.":[63],"This":[64],"paper":[65],"presents":[66],"way":[68],"perform":[70],"this":[71,149],"translation.":[72],"We":[73,129],"map":[74],"ANN":[76,106,184],"weights":[77,96],"SNN":[79,110,138],"synapses":[80],"layer-by-layer":[81],"by":[82,144],"forming":[83],"least-square-error":[85],"approximation":[86],"problem":[87],"at":[88],"each":[89],"layer.":[90],"An":[91],"optimal":[92,132],"set":[93,143],"synapse":[95],"then":[98],"found":[100],"for":[101],"given":[103],"choice":[104],"activation":[107],"function":[108],"neuron.":[111],"Using":[112],"an":[113,131],"appropriate":[114],"constrained":[115],"solver,":[116],"we":[117,151],"generate":[119],"SNNs":[120],"compatible":[121],"with":[122,181,199],"digital,":[123],"analog,":[124],"or":[125],"hybrid":[126],"chip":[127],"architectures.":[128],"present":[130],"node":[133],"pruning":[134],"method":[135],"allow":[137],"layer":[139],"sizes":[140],"designer.":[146],"To":[147],"illustrate":[148],"process,":[150],"convert":[152],"three":[153,163],"ANNs,":[154],"including":[155],"one":[156],"convolutional":[157],"network,":[158],"SNNs.":[160],"In":[161],"all":[162],"cases,":[164],"simple":[166],"linear":[167],"program":[168],"solver":[169],"was":[170],"used.":[171],"The":[172,192],"experiments":[173],"show":[174],"that":[175],"resulting":[177],"maintain":[179],"agreement":[180],"original":[183],"excellent":[186],"performance":[187],"evaluation":[190],"tasks.":[191],"were":[194],"also":[195],"reduced":[196],"size":[198],"little":[200],"loss":[201],"task":[203],"performance.":[204]},"counts_by_year":[],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2018-02-23T00:00:00"}