{"id":"https://openalex.org/W4285676450","doi":"https://doi.org/10.1145/3531437.3539706","title":"HOGEye: Neural Approximation of HOG Feature Extraction in RRAM-Based 3D-Stacked Image Sensors","display_name":"HOGEye: Neural Approximation of HOG Feature Extraction in RRAM-Based 3D-Stacked Image Sensors","publication_year":2022,"publication_date":"2022-07-16","ids":{"openalex":"https://openalex.org/W4285676450","doi":"https://doi.org/10.1145/3531437.3539706"},"language":"en","primary_location":{"id":"doi:10.1145/3531437.3539706","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3531437.3539706","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3531437.3539706","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the ACM/IEEE International Symposium on Low Power Electronics and Design","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3531437.3539706","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5075420600","display_name":"Tianrui Ma","orcid":"https://orcid.org/0000-0003-0894-6653"},"institutions":[{"id":"https://openalex.org/I204465549","display_name":"Washington University in St. Louis","ror":"https://ror.org/01yc7t268","country_code":"US","type":"education","lineage":["https://openalex.org/I204465549"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Tianrui Ma","raw_affiliation_strings":["Washington University in St. Louis, United States"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Washington University in St. Louis, United States","institution_ids":["https://openalex.org/I204465549"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062801966","display_name":"Weidong Cao","orcid":"https://orcid.org/0000-0001-7539-8250"},"institutions":[{"id":"https://openalex.org/I204465549","display_name":"Washington University in St. Louis","ror":"https://ror.org/01yc7t268","country_code":"US","type":"education","lineage":["https://openalex.org/I204465549"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Weidong Cao","raw_affiliation_strings":["Washington University in St. Louis, United States"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Washington University in St. Louis, United States","institution_ids":["https://openalex.org/I204465549"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5063901096","display_name":"Fei Qiao","orcid":"https://orcid.org/0000-0002-5054-9590"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fei Qiao","raw_affiliation_strings":["Tsinghua University, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5050566186","display_name":"Ayan Chakrabarti","orcid":"https://orcid.org/0000-0002-4843-740X"},"institutions":[{"id":"https://openalex.org/I204465549","display_name":"Washington University in St. Louis","ror":"https://ror.org/01yc7t268","country_code":"US","type":"education","lineage":["https://openalex.org/I204465549"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ayan Chakrabarti","raw_affiliation_strings":["Washington University in St. Louis, United States"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Washington University in St. Louis, United States","institution_ids":["https://openalex.org/I204465549"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5010392575","display_name":"Xuan Zhang","orcid":"https://orcid.org/0000-0002-0482-5435"},"institutions":[{"id":"https://openalex.org/I204465549","display_name":"Washington University in St. Louis","ror":"https://ror.org/01yc7t268","country_code":"US","type":"education","lineage":["https://openalex.org/I204465549"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xuan Zhang","raw_affiliation_strings":["Washington University in St. Louis, United States"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Washington University in St. Louis, United States","institution_ids":["https://openalex.org/I204465549"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":13,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11992","display_name":"CCD and CMOS Imaging Sensors","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/T11992","display_name":"CCD and CMOS Imaging Sensors","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/T10502","display_name":"Advanced Memory and Neural Computing","score":0.9998000264167786,"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/T11128","display_name":"Transition Metal Oxide Nanomaterials","score":0.9968000054359436,"subfield":{"id":"https://openalex.org/subfields/2507","display_name":"Polymers and Plastics"},"field":{"id":"https://openalex.org/fields/25","display_name":"Materials Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/histogram","display_name":"Histogram","score":0.7586150169372559},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.7289162278175354},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7134963274002075},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6736409068107605},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.6378321647644043},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5993862152099609},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.574516236782074},{"id":"https://openalex.org/keywords/image-sensor","display_name":"Image sensor","score":0.4871932864189148},{"id":"https://openalex.org/keywords/resistive-random-access-memory","display_name":"Resistive random-access memory","score":0.4589802324771881},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4539366662502289},{"id":"https://openalex.org/keywords/pipeline-transport","display_name":"Pipeline transport","score":0.45054352283477783},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.37401068210601807},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.2836626172065735},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.1800556778907776},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.15674817562103271},{"id":"https://openalex.org/keywords/electrical-engineering","display_name":"Electrical engineering","score":0.07425177097320557}],"concepts":[{"id":"https://openalex.org/C53533937","wikidata":"https://www.wikidata.org/wiki/Q185020","display_name":"Histogram","level":3,"score":0.7586150169372559},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.7289162278175354},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7134963274002075},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6736409068107605},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.6378321647644043},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5993862152099609},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.574516236782074},{"id":"https://openalex.org/C76935873","wikidata":"https://www.wikidata.org/wiki/Q209121","display_name":"Image sensor","level":2,"score":0.4871932864189148},{"id":"https://openalex.org/C182019814","wikidata":"https://www.wikidata.org/wiki/Q1143830","display_name":"Resistive random-access memory","level":3,"score":0.4589802324771881},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4539366662502289},{"id":"https://openalex.org/C175309249","wikidata":"https://www.wikidata.org/wiki/Q725864","display_name":"Pipeline transport","level":2,"score":0.45054352283477783},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.37401068210601807},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2836626172065735},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.1800556778907776},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.15674817562103271},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.07425177097320557},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C87717796","wikidata":"https://www.wikidata.org/wiki/Q146326","display_name":"Environmental engineering","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3531437.3539706","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3531437.3539706","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3531437.3539706","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the ACM/IEEE International Symposium on Low Power Electronics and Design","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3531437.3539706","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3531437.3539706","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3531437.3539706","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the ACM/IEEE International Symposium on Low Power Electronics and Design","raw_type":"proceedings-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/7","display_name":"Affordable and clean energy","score":0.9100000262260437}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4285676450.pdf","grobid_xml":"https://content.openalex.org/works/W4285676450.grobid-xml"},"referenced_works_count":26,"referenced_works":["https://openalex.org/W1983740965","https://openalex.org/W1988115241","https://openalex.org/W2008311222","https://openalex.org/W2069756028","https://openalex.org/W2076496703","https://openalex.org/W2133728753","https://openalex.org/W2141546789","https://openalex.org/W2142454444","https://openalex.org/W2154740476","https://openalex.org/W2155707315","https://openalex.org/W2161969291","https://openalex.org/W2486080740","https://openalex.org/W2582903495","https://openalex.org/W2787701666","https://openalex.org/W2954181867","https://openalex.org/W2967435224","https://openalex.org/W2974134299","https://openalex.org/W2997273453","https://openalex.org/W3015271406","https://openalex.org/W3081228736","https://openalex.org/W3133725905","https://openalex.org/W3211168157","https://openalex.org/W4251607986","https://openalex.org/W4291625416","https://openalex.org/W6680936074","https://openalex.org/W6766971063"],"related_works":["https://openalex.org/W2545245183","https://openalex.org/W2054635671","https://openalex.org/W2017425642","https://openalex.org/W2350916061","https://openalex.org/W1970117475","https://openalex.org/W3161624601","https://openalex.org/W2611512961","https://openalex.org/W2078381924","https://openalex.org/W4206468571","https://openalex.org/W4381388454"],"abstract_inverted_index":{"Many":[0],"computer":[1],"vision":[2],"tasks,":[3],"ranging":[4],"from":[5],"recognition":[6],"to":[7,33,116,145],"multi-view":[8],"registration,":[9],"operate":[10],"on":[11],"feature":[12,56],"representation":[13],"of":[14,59,99,110,140],"images":[15],"rather":[16],"than":[17,133,156,162],"raw":[18],"pixel":[19],"intensities.":[20],"However,":[21],"conventional":[22],"pipelines":[23],"for":[24,53,136],"obtaining":[25],"these":[26],"representations":[27],"incur":[28],"significant":[29,118],"energy":[30,119,159],"consumption":[31],"due":[32],"pixel-wise":[34],"analog-to-digital":[35],"(A/D)":[36],"conversions":[37,112],"and":[38,41,72,102,105,107],"costly":[39],"storage":[40],"computations.":[42],"In":[43],"this":[44],"paper,":[45],"we":[46],"propose":[47],"HOGEye,":[48],"an":[49,137],"efficient":[50],"near-pixel":[51],"implementation":[52],"a":[54,83,89],"widely-used":[55],"extraction":[57,70],"algorithm\u2014Histograms":[58],"Oriented":[60],"Gradients":[61],"(HOG).":[62],"HOGEye":[63,114,128],"moves":[64],"the":[65,78,108,126,148,163],"key":[66],"but":[67],"computation-intensive":[68],"derivative":[69],"(DE)":[71],"histogram":[73],"generation":[74],"(HG)":[75],"steps":[76],"into":[77],"analog":[79],"domain":[80],"by":[81],"applying":[82],"novel":[84],"neural":[85],"approximation":[86],"method":[87],"in":[88],"resistive":[90],"random-access":[91],"memory":[92],"(RRAM)-based":[93],"3D-stacked":[94],"image":[95,138],"sensor.":[96],"The":[97],"co-location":[98],"perception":[100],"(sensor)":[101],"computation":[103],"(DE":[104],"HG)":[106],"alleviation":[109],"A/D":[111],"allow":[113],"design":[115],"achieve":[117],"saving.":[120],"With":[121],"negligible":[122],"detection":[123],"rate":[124],"degradation,":[125],"entire":[127],"sensor":[129],"system":[130],"consumes":[131,152],"less":[132],"48\u03bc[email":[134],"protected]":[135],"resolution":[139],"256":[141,143],"\u00d7":[142,158],"(equivalent":[144],"24.3pJ/pixel)":[146],"while":[147],"processing":[149],"part":[150],"only":[151],"14.1pJ/pixel,":[153],"achieving":[154],"more":[155],"2.5":[157],"efficiency":[160],"improvement":[161],"state-of-the-art":[164],"designs.":[165]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}