{"id":"https://openalex.org/W4415003703","doi":"https://doi.org/10.1109/mlcad65511.2025.11189167","title":"RTLExplain: A Structured Approach to RTL Code Summarization and Question Answering for Medium-to-Large Designs Using LLMs","display_name":"RTLExplain: A Structured Approach to RTL Code Summarization and Question Answering for Medium-to-Large Designs Using LLMs","publication_year":2025,"publication_date":"2025-09-08","ids":{"openalex":"https://openalex.org/W4415003703","doi":"https://doi.org/10.1109/mlcad65511.2025.11189167"},"language":"en","primary_location":{"id":"doi:10.1109/mlcad65511.2025.11189167","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mlcad65511.2025.11189167","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 ACM/IEEE 7th Symposium on Machine Learning for CAD (MLCAD)","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/A5104207721","display_name":"Ting-Hsun Chi","orcid":null},"institutions":[{"id":"https://openalex.org/I16733864","display_name":"National Taiwan University","ror":"https://ror.org/05bqach95","country_code":"TW","type":"education","lineage":["https://openalex.org/I16733864"]}],"countries":["TW"],"is_corresponding":true,"raw_author_name":"Ting-Hsun Chi","raw_affiliation_strings":["National Taiwan University,Department of Electrical Engineering,Taipei,Taiwan"],"affiliations":[{"raw_affiliation_string":"National Taiwan University,Department of Electrical Engineering,Taipei,Taiwan","institution_ids":["https://openalex.org/I16733864"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015811754","display_name":"Charles Mackin","orcid":"https://orcid.org/0000-0001-8413-5583"},"institutions":[{"id":"https://openalex.org/I1341412227","display_name":"IBM (United States)","ror":"https://ror.org/05hh8d621","country_code":"US","type":"company","lineage":["https://openalex.org/I1341412227"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Charles Mackin","raw_affiliation_strings":["IBM Research,San Jose,CA,USA"],"affiliations":[{"raw_affiliation_string":"IBM Research,San Jose,CA,USA","institution_ids":["https://openalex.org/I1341412227"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5043026220","display_name":"Luyao Shi","orcid":"https://orcid.org/0000-0002-4703-3294"},"institutions":[{"id":"https://openalex.org/I1341412227","display_name":"IBM (United States)","ror":"https://ror.org/05hh8d621","country_code":"US","type":"company","lineage":["https://openalex.org/I1341412227"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Luyao Shi","raw_affiliation_strings":["IBM Research,San Jose,CA,USA"],"affiliations":[{"raw_affiliation_string":"IBM Research,San Jose,CA,USA","institution_ids":["https://openalex.org/I1341412227"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5106958940","display_name":"Prashanth Vijayaraghavan","orcid":"https://orcid.org/0000-0002-9981-3954"},"institutions":[{"id":"https://openalex.org/I1341412227","display_name":"IBM (United States)","ror":"https://ror.org/05hh8d621","country_code":"US","type":"company","lineage":["https://openalex.org/I1341412227"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Prashanth Vijayaraghavan","raw_affiliation_strings":["IBM Research,San Jose,CA,USA"],"affiliations":[{"raw_affiliation_string":"IBM Research,San Jose,CA,USA","institution_ids":["https://openalex.org/I1341412227"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5086723230","display_name":"Hsinyu Tsai","orcid":"https://orcid.org/0000-0002-3971-097X"},"institutions":[{"id":"https://openalex.org/I1341412227","display_name":"IBM (United States)","ror":"https://ror.org/05hh8d621","country_code":"US","type":"company","lineage":["https://openalex.org/I1341412227"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hsinyu Tsai","raw_affiliation_strings":["IBM Research,San Jose,CA,USA"],"affiliations":[{"raw_affiliation_string":"IBM Research,San Jose,CA,USA","institution_ids":["https://openalex.org/I1341412227"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5069189041","display_name":"Ehsan Degan","orcid":null},"institutions":[{"id":"https://openalex.org/I1341412227","display_name":"IBM (United States)","ror":"https://ror.org/05hh8d621","country_code":"US","type":"company","lineage":["https://openalex.org/I1341412227"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ehsan Degan","raw_affiliation_strings":["IBM Research,San Jose,CA,USA"],"affiliations":[{"raw_affiliation_string":"IBM Research,San Jose,CA,USA","institution_ids":["https://openalex.org/I1341412227"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5104207721"],"corresponding_institution_ids":["https://openalex.org/I16733864"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.15466212,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"7"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10181","display_name":"Natural Language Processing Techniques","score":0.9889000058174133,"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/T10181","display_name":"Natural Language Processing Techniques","score":0.9889000058174133,"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/T10260","display_name":"Software Engineering Research","score":0.9573000073432922,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T11450","display_name":"Model-Driven Software Engineering Techniques","score":0.9121999740600586,"subfield":{"id":"https://openalex.org/subfields/1712","display_name":"Software"},"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/automatic-summarization","display_name":"Automatic summarization","score":0.7544000148773193},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.5992000102996826},{"id":"https://openalex.org/keywords/question-answering","display_name":"Question answering","score":0.5942999720573425},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.49070000648498535},{"id":"https://openalex.org/keywords/limiting","display_name":"Limiting","score":0.3806000053882599}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8050000071525574},{"id":"https://openalex.org/C170858558","wikidata":"https://www.wikidata.org/wiki/Q1394144","display_name":"Automatic summarization","level":2,"score":0.7544000148773193},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.5992000102996826},{"id":"https://openalex.org/C44291984","wikidata":"https://www.wikidata.org/wiki/Q1074173","display_name":"Question answering","level":2,"score":0.5942999720573425},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.49070000648498535},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.43880000710487366},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.4059000015258789},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.39320001006126404},{"id":"https://openalex.org/C188198153","wikidata":"https://www.wikidata.org/wiki/Q1613840","display_name":"Limiting","level":2,"score":0.3806000053882599},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.335099995136261},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.3091000020503998},{"id":"https://openalex.org/C2778012447","wikidata":"https://www.wikidata.org/wiki/Q1034415","display_name":"Scope (computer science)","level":2,"score":0.30660000443458557},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2556000053882599},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.2531000077724457}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/mlcad65511.2025.11189167","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mlcad65511.2025.11189167","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 ACM/IEEE 7th Symposium on Machine Learning for CAD (MLCAD)","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":12,"referenced_works":["https://openalex.org/W347636100","https://openalex.org/W2148972377","https://openalex.org/W3090045546","https://openalex.org/W4252076394","https://openalex.org/W4389166737","https://openalex.org/W4391681217","https://openalex.org/W4393145520","https://openalex.org/W4403024007","https://openalex.org/W4404102068","https://openalex.org/W4404134046","https://openalex.org/W4409282805","https://openalex.org/W4413822481"],"related_works":[],"abstract_inverted_index":{"Large":[0],"Language":[1],"Models":[2],"(LLMs)":[3],"show":[4,132],"promise":[5],"in":[6,35],"assisting":[7],"with":[8,140],"Register":[9],"Transfer":[10],"Level":[11],"(RTL)":[12],"design":[13,79],"tasks,":[14],"including":[15],"code":[16,48,95],"summarization,":[17],"documentation,":[18],"and":[19,58,86,151],"question":[20,128],"answering.":[21],"However,":[22],"directly":[23],"applying":[24],"LLMs":[25,42],"to":[26,32,46,54,73,122,148,154],"entire":[27],"RTL":[28,47,78,108],"codebases":[29],"often":[30],"leads":[31],"low":[33],"accuracy":[34,144],"these":[36,119],"tasks.":[37,80,130],"This":[38],"is":[39,83],"primarily":[40],"because":[41],"are":[43,114],"less":[44],"exposed":[45],"during":[49],"pretraining,":[50],"limiting":[51],"their":[52],"ability":[53],"understand":[55],"RTL-specific":[56],"semantics":[57],"structural":[59],"dependencies.":[60],"To":[61],"overcome":[62],"this":[63],"challenge,":[64],"we":[65,117],"propose":[66],"RTLExplain,":[67],"which":[68],"builds":[69],"project-specific":[70],"knowledge":[71,100,120,136],"bases":[72,101,121],"enhance":[74],"LLM":[75],"performance":[76],"on":[77,94],"Our":[81],"method":[82],"entirely":[84],"offline":[85],"requires":[87],"no":[88],"additional":[89],"training":[90],"or":[91],"fine-tuning.":[92],"Experiments":[93],"summarization":[96],"using":[97],"the":[98],"generated":[99],"demonstrate":[102],"consistent":[103],"improvements":[104],"across":[105],"various":[106],"medium-to-large":[107],"projects,":[109],"even":[110],"when":[111,138],"variable":[112],"names":[113],"obfuscated.":[115],"Furthermore,":[116],"use":[118],"support":[123],"Retrieval-Augmented":[124],"Generation":[125],"(RAG)":[126],"for":[127],"answering":[129],"Results":[131],"that":[133],"our":[134],"enhanced":[135],"bases,":[137],"combined":[139],"RAG,":[141],"improve":[142],"question-answering":[143],"by":[145],"37%":[146],"compared":[147,153],"na\u00efve":[149],"prompting":[150],"27%":[152],"conventional":[155],"RAG.":[156]},"counts_by_year":[],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-10-10T00:00:00"}