[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119708-en":3,"doc-seo-119708-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119708,1099513958762,"Logic","https://ap-avatar.wpscdn.com/avatar/1000023916a998db790?x-image-process=image/resize,m_fixed,w_180,h_180&k=1784791008015729253",8,"Research & Report","Machine Learning for Microprocessor Performance Bug Localization","Microprocessor validation demands substantial engineering time, and performance bugs that harm overall speed without breaking functional correctness are especially difficult to diagnose due to the absence of a golden reference for correct performance. This work presents two machine-learning-based automated methodologies to localize such performance bugs. Results show that, for evaluated core performance bugs with average IPC impact above 1%, the best method identifies the exact microarchitectural unit 77% of the time, and achieves top-3 unit accuracy over 90% among 11 locations. The inference completes within seconds, reducing debugging time.","Machine Learning for Microprocessor Performance  \nBug Localization  \nErick Carvajal Barboza􀀃 , Mahesh Ketkar†, Michael Kishinevsky†, Paul Gratz‡, and Jiang Hu‡  \n􀀃 Universidad de Costa Rica, †Texas A&M University, ‡Intel Corportation  \nCorresponding Author: [erick.carvajalbarboza@ucr.ac.cr](erick.carvajalbarboza@ucr.ac.cr)  \narXiv :2303 . 15280v1 [ cs .AR] 27 Mar 2023  \nAbstract—The validation process for microprocessors is a very complex task that consumes substantial engineering time during the design process. Bugs that degrade overall system performance, without affecting its functional correctness, are particularly difficult to debug given the lack of a golden reference for bug-free performance. This work introduces two automated performance bug localization methodologies based on machine learning that aim to aid the debugging process. Our results show that, for the evaluated microprocessor core performance bugs whose average IPC impact is greater than 1%, our best performing technique is able to localize the exact microarchitectural unit of the bug 􀀘 77% of the time, while achieving a top-3 unit accuracy (out of 11 possible locations) of over 90% for bugs with the same average IPC impact. The proposed system in our simulation setup requires only a few seconds to perform a bug location inference, which leads to a reduced debugging time.  \nI. INTRODUCTION  \nLarge amounts of time and effort are devoted to verification and validation of every microprocessor design project. Broadly, design verification can be broken into two large categories:  \n(1) functional and (2) performance verification, which is to identify design bugs that degrade performance without affecting functionality. Performance bugs are different from performance bottleneck as the former is due to design mistakes while the later is caused by tight resource constraints. Performance loss due to performance bugs can be very significant, with recent reported cases shown to be > 10%[34] . This demonstratesa critical need for automated mechanisms for performance debugging. As recent designs from Intel [26], AMD [5], ARM [12], and others place an even greater emphasis on core performance, design complexity has scaled dramatically, likewise scaling the difficulty in all forms of verification.  \nPerformance verification at microarchitecture level ensures that a design correctly achieves expected performance in terms of execution time or cycle count. The main challenge in this task is that, unlike functional verification, there is no exact golden reference to compare against. This is because of the high difficulty of modeling all the interactions between the different units in complex microprocessor designs, and accurately represent how they affect the overall system performance.  \nTraditionally, performance verification is conducted mostly through manual techniques which rely on rough estimations of performance gain expected by microarchitectural changes [41] . Such manual processes are not only very lengthy but also error-prone.  \nThe process of performance verification and debugging roughly consists of two steps: (1) detection, which determines whether a design achieves expected performance or not, and (2) localization, which identifies the microarchitectural units  \ncausing the performance issues and is the focus of this work.  \nThere are few previous studies on automating detection of microprocessor performance bugs [15], [17], [42] . The majority of those [15], [42] relies on capturing design intentions using a bespoke performance model as a golden reference, this entails long development time and may contain errors by itself. Recently, a data driven and machine learning (ML)-based approach [17] was developed for automatic performance bug detection with high accuracy. Although significant, these works do not solve the problem of performance bug localization.  \nWorks in automating microprocessor performance bug localization are even scarcer. Adir et al. [4] propose perhaps the on","cbCaijHt4BW3Rqox","https://ap.wps.com/l/cbCaijHt4BW3Rqox","pdf",994009,1,12,"English","en",105,"# Abstract\n# Introduction\n## Performance and functional verification\n## Challenge: no golden reference\n## Manual techniques and their limitations\n## Detection vs localization\n## Related work\n## Contributions and approach","[{\"question\":\"Why are performance bugs harder to debug than functional bugs in microprocessors?\",\"answer\":\"Performance bugs degrade overall system performance without affecting functional correctness, and there is no golden reference performance model to compare against. Modeling all interactions between microarchitecture units is also difficult, which complicates diagnosis.\"},{\"question\":\"What do the proposed machine-learning methodologies do?\",\"answer\":\"They automatically generate a ranked list of the most likely microarchitectural units containing the performance bug. This helps prioritize debugging order and identify teams with relevant expertise.\"},{\"question\":\"What accuracy and speed does the best technique achieve in the reported evaluation?\",\"answer\":\"For core performance bugs with average IPC impact greater than 1%, the best technique localizes the exact microarchitectural unit 77% of the time, and provides over 90% top-3 accuracy among 11 locations. The system requires only a few seconds for bug location inference in the simulation setup.\"}]","Machine Learning for Microprocessor Performance Bug Localization | PDF",1785725889,30,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-for-microprocessor-performance-bug-localization","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-for-microprocessor-performance-bug-localization/119708/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why are performance bugs harder to debug than functional bugs in microprocessors?","Question",{"text":75,"@type":76},"Performance bugs degrade overall system performance without affecting functional correctness, and there is no golden reference performance model to compare against. Modeling all interactions between microarchitecture units is also difficult, which complicates diagnosis.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What do the proposed machine-learning methodologies do?",{"text":80,"@type":76},"They automatically generate a ranked list of the most likely microarchitectural units containing the performance bug. This helps prioritize debugging order and identify teams with relevant expertise.",{"name":82,"@type":73,"acceptedAnswer":83},"What accuracy and speed does the best technique achieve in the reported evaluation?",{"text":84,"@type":76},"For core performance bugs with average IPC impact greater than 1%, the best technique localizes the exact microarchitectural unit 77% of the time, and provides over 90% top-3 accuracy among 11 locations. 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