[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117480-en":3,"doc-seo-117480-105":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},117480,1099514067415,"Rowan","https://ap-avatar.wpscdn.com/avatar/100002539d78ffe74a7?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779092875211072502",8,"Research & Report","Dataset Generation for Cache Design Using Machine Learning - Thesis","Machine learning’s ability to learn from training data and produce accurate outputs has expanded across many technology industries beyond computer science and engineering. This thesis proposes a practical workflow for microarchitectural assessment by generating a tailored dataset aimed at cache design. Simulations explore how varying one or more cache levels affects performance under contemporary technologies, and results distinguish between configurations. Using the generated dataset, the work targets a model that configures cache parameters to meet or exceed specified performance goals.","Dataset Generation for Cache Design Using Machine Learning  \nA THESIS  \nPresented to the University Honors Program California State University, Long Beach  \nIn Partial Fulfillment  \nof the Requirements for the University Honors Program Certificate  \nJeremy San Miguel  \nSpring 2025  \nI, THE UNDERSIGNED MEMBER OF THE COMMITTEE, HAVE APPROVED THIS THESIS  \nDATASET GENERATION FOR CACHE DESIGN USING MACHINE LEARNING  \nBY  \nJeremy San Miguel  \n5/16/2025  \nJelena Trajkovic, Ph.D. (Thesis Advisor) CECS Department  \nCalifornia State University, Long Beach  \nSpring 2025  \nABSTRACT  \nDataset Generation for Cache Design Using Machine Learning  \nBy  \nJeremy San Miguel  \nSpring 2025  \nThe use of machine learning has gained widespread adoption for its ability to be trained and to output what we desire across many fields of technology. Its scope has extended far beyond its use in computer science and engineering, being applicable across a wider range of industries. Its ability to adapt and improve on existing technologies allows it to be further scalable and precision accurate if it is given information to train with.  \nThis work proposes a practical use of machine learning by contributing a tailored dataset specifically for use within microarchitectural assessment. Simulations of various parameters are conducted with contemporary technologies to determine the most effective variation of one or multiple cache levels. Our experimental results show distinctions between varying configurations of cache simulation. With proper machine learning based on our dataset, we aim for a machine learning model to configure suitable cache parameters to fulfill or exceed performance goals.  \nACKNOWLEDGEMENTS  \nI would like to personally express my thanks to Jelena Trajkovic, my thesis advisor. Iam forever thankful for her patience, punctual feedback, and reassurance that helped shape this paper and make this work possible. I would also like to formally thank Joseph Chorbajian for guidance within cache simulation as well as their contributions to outsourcing benchmarks and result exportation from ChampSim. Lastly, I would like to thank my parents and friends for inspiring me to do my very best in what I do.  \nTABLE OF CONTENTS  \nPage  \nACKNOWLEDGEMENTS .............................................................................................. iii  \n[LIST OF TABLES............................................................................................................ vi](LIST OF TABLES............................................................................................................ vi)  \n[LIST OF FIGURES ...............................](LIST OF FIGURES ...............................).......................................................................... vii  \nCHAPTER  \n1. INTRODUCTION ........................................................................................................... 1  \nMotivation ......................................................................................................................... 2  \n2. BACKGROUND & RELATED WORK ...................................................................... 3  \nCache Memory................................................................................................................. 3  \nCache Replacement Policy............................................................................................. 7  \nWrite Back Policy ........................................................................................................... 8  \nImproving Cache Replacement Policies Using Machine Learning Techniques .... 9  \nMachine Learning-Driven Cache Policy Designs ...................................................... 10  \n3. PROPOSED WORK........................................................................................................ 11  \nBenchmarks ...................................................................................................................... 11","cbCaibbkKDppoh53","https://ap.wps.com/l/cbCaibbkKDppoh53","pdf",553531,1,30,"English","en",105,"# Acknowledgements\n# List of Tables\n# List of Figures\n# Chapter 1. Introduction\n## Motivation\n# Chapter 2. Background & Related Work\n## Cache Memory\n## Cache Replacement Policy\n## Write Back Policy\n## Improving Cache Replacement Policies Using Machine Learning Techniques\n## Machine Learning-Driven Cache Policy Designs\n# Chapter 3. Proposed Work\n## Benchmarks\n## Benchmark Traces\n## ChampSim\n## Configurations\n# Chapter 4. Experimental Results\n## Dataset Generation\n## Dataset Evaluation\n## Run Time\n# Chapter 5. Conclusion\n## Future Direction\n# References","[{\"question\":\"What does the thesis propose for cache design using machine learning?\",\"answer\":\"It proposes generating a tailored dataset specifically for microarchitectural assessment of cache design. The dataset supports building a machine learning model for selecting cache parameters.\"},{\"question\":\"How are cache configurations evaluated in the thesis?\",\"answer\":\"The work runs simulations over various parameters and cache level variations using contemporary technologies. Experimental results compare distinctions across different cache simulation configurations.\"},{\"question\":\"What is the intended outcome of training with the generated dataset?\",\"answer\":\"With proper machine learning based on the dataset, the goal is a model that configures cache parameters to fulfill or exceed performance goals.\"}]","Dataset Generation for Cache Design Using Machine Learning - Thesis | PDF",1785676100,76,{"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},"dataset-generation-for-cache-design-using-machine-learning-thesis","",{"@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/dataset-generation-for-cache-design-using-machine-learning-thesis/117480/",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","2026-08-02",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},"What does the thesis propose for cache design using machine learning?","Question",{"text":75,"@type":76},"It proposes generating a tailored dataset specifically for microarchitectural assessment of cache design. The dataset supports building a machine learning model for selecting cache parameters.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How are cache configurations evaluated in the thesis?",{"text":80,"@type":76},"The work runs simulations over various parameters and cache level variations using contemporary technologies. Experimental results compare distinctions across different cache simulation configurations.",{"name":82,"@type":73,"acceptedAnswer":83},"What is the intended outcome of training with the generated dataset?",{"text":84,"@type":76},"With proper machine learning based on the dataset, the goal is a model that configures cache parameters to fulfill or exceed performance goals.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":21,"slug":121},"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]