[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122571-en":3,"doc-seo-122571-105":30,"detail-sidebar-cat-0-en-105":92},{"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":20,"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},122571,7971461741311,"Ophelia","https://ap-avatar.wpscdn.com/avatar/74000253aff267980c6?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779345379180704826",8,"Research & Report","Machine Learning based Memory Load Approximation - Doctor of Philosophy Thesis","Modern computing requires ever higher performance and energy efficiency, yet conventional processors often stall waiting for memory data, forming the memory wall bottleneck. Existing solutions such as prefetching, load value prediction, and caching provide limited benefits and can fail on irregular, data-driven workloads common in contemporary multimedia and machine learning. This thesis proposes ML-based Load Value Approximation (ML-LVA) grounded in approximate computing, training an offline compact predictor to estimate memory load values and reduce access latency. ML-LVA is implemented in both software and hardware, accelerates memory accesses over 6×, and delivers application speedups up to 2.45× with perceptual fidelity.","Machine Learning based Memory Load Approximation  \nAlain Aoun  \nA Thesis  \nin  \nThe Department  \nof  \nElectrical and Computer Engineering  \nPresented in Partial Fulfillment of the Requirements for the Degree of  \nDoctor of Philosophy (Electrical and Computer Engineering) at  \nConcordia University  \nMontréal, Québec, Canada  \nJuly 2025  \n© Alain Aoun, 2025  \nConcordia University  \nSchool of Graduate Studies  \nThis is to certify that the thesis prepared  \nBy: Mr. Alain Aoun  \nEntitled: Machine Learning based Memory Load Approximation  \nand submitted in partial fulfillment of the requirements for the degree of  \nDoctor of Philosophy (Electrical and Computer Engineering)  \ncomplies with the regulations of this University and meets the accepted standards with respect to originality and quality.  \nSigned by the Final Examining Committee:  \n  Chair  \nDr. Name of the Chair  \n  External Examiner Dr. Jean Pierre David  \n  Examiner  \nDr. Otmane Ait Mohamed  \n  Examiner  \nDr. Sebastien Lebeux  \n  Examiner  \nDr. Joey Paquet  \n  Supervisor  \nDr. Sofiène Tahar  \nApproved by  Abdelwahab Hamou-Lhadj, Chair   \nDepartment of Electrical and Computer Engineering  \nJuly 2025  Mourad Debbabi, Dean  Faculty of Engineering and Computer Science  \nAbstract  \nMachine Learning based Memory Load Approximation  \nAlain Aoun, Ph.D.  \nConcordia University, 2025  \nModern computing applications demand ever-increasing performance and energy efficiency. However, conventional processor architectures frequently stall while waiting for data retrieval from memory, creating a bottleneck known as the memory wall. Over the past decades, various approaches such as speculative prefetching, load value prediction, and hardware caching have been proposed to mitigate this limitation. While these techniques yield moderate gains, they often rely on rigid hardware logic or simple pattern matching, which struggle with the irregular, data-driven workloads typical of contemporary multimedia and machine learning applications.  \nThis thesis propose to use Machine Learning (ML) to speculate load values and reduce memory accesses. The proposed method is grounded in the principles of Approximate Computing (AC), where minor inaccuracies are accepted in exchange for improvements in performance or efficiency. To this end, we introduce an ML-based Load Value Approximation (ML-LVA) approach, which predicts the values of memory loads to reduce access latency. The ML-LVA is trained offline to generate a compact predictor that captures patterns in image and audio data, enabling accurate value prediction during runtime without the need for continual retraining. By learning  \nspatial correlations among adjacent data values, the proposed ML-LVA effectively anticipates memory contents, thereby reducing stalls and improving overall system performance in online deployment.  \nWe have implemented the proposed ML-LVA framework both in software and hardware. The software variant targets existing processors lacking reconfigurability, as well as systems with tight area or power constraints that prohibit adding custom hardware. It operates as a callable subroutine designed for seamless integration without modifying the processor architecture. The software implementation was tested on an x86 processor in the GEM5 simulator. On the other hand, the hardware-based implementation integrates the proposed ML-LVA as a dedicated accelerator accessed via a custom instruction, offering tighter pipeline integration, lower latency, and enhanced efficiency for newly designed systems. The hardware-based ML-LVA was implemented in CVA6, which is an open source RISC-V processor. The synthesis results conducted in Cadence Innovus showed that the overhead of the added accelerator is marginal.  \nExperimental results conducted on audio and image processing workloads demonstrate that the proposed ML-LVA accelerates memory access by over 6 × , resulting in application speedups up to 2.45 × . Additionally, even when predicting up to","cbCaigwmAac4TjK6","https://ap.wps.com/l/cbCaigwmAac4TjK6","pdf",4185029,1,166,"English","en",105,"# Abstract\n## Problem: the memory wall\n## Proposed method: ML-LVA and approximate computing\n## Implementations: software and hardware\n## Experimental evaluation on audio and image workloads","[{\"question\":\"What problem does this thesis address?\",\"answer\":\"It addresses the memory wall, where processors stall while waiting for memory data, limiting performance and energy efficiency.\"},{\"question\":\"How does ML-LVA work?\",\"answer\":\"ML-LVA uses machine learning to approximate memory load values. An offline-trained compact predictor captures patterns to enable accurate runtime speculation and reduce stalls.\"},{\"question\":\"How are results validated in this work?\",\"answer\":\"The framework is implemented in both software and hardware and evaluated on audio and image processing workloads, showing memory-access acceleration over 6× and speedups up to 2.45×.\"}]","Machine Learning based Memory Load Approximation - Doctor of Philosophy Thesis | PDF",1785811373,418,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":28},"machine-learning-based-memory-load-approximation-doctor-of-philosophy-thesis","",{"@graph":36,"@context":86},[37,54,69],{"@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-based-memory-load-approximation-doctor-of-philosophy-thesis/122571/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05","2026-08-04",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What problem does this thesis address?","Question",{"text":76,"@type":77},"It addresses the memory wall, where processors stall while waiting for memory data, limiting performance and energy efficiency.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How does ML-LVA work?",{"text":81,"@type":77},"ML-LVA uses machine learning to approximate memory load values. An offline-trained compact predictor captures patterns to enable accurate runtime speculation and reduce stalls.",{"name":83,"@type":74,"acceptedAnswer":84},"How are results validated in this work?",{"text":85,"@type":77},"The framework is implemented in both software and hardware and evaluated on audio and image processing workloads, showing memory-access acceleration over 6× and speedups up to 2.45×.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":46,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":46,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]