[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121538-en":3,"doc-seo-121538-105":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},121538,687197207919,"Theodora","https://ap-avatar.wpscdn.com/avatar/a000253d6f5f7c60be?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779446848396160552",6,"Technology","Non-Volatile Memories - Challenges and Opportunities for Embedded System Architectures with Focus on Machine Learning Applications","This paper explores the challenges and opportunities of integrating non-volatile memories (NVMs) into embedded systems for machine learning. It highlights benefits including higher memory density, lower power consumption, persistence, and compute-in-memory capability. The discussion emphasizes intermittent computing, where energy availability varies and persistence closer to the CPU can reduce latency and energy overhead. It also examines computation in resistive NVMs and memory-centric machine learning using improved cache behavior and sparsity.","2023 International Conference on Compilers, Architecture, and Synthesis for Embedded Systems (CASES)  \nSpecial Session-Non-Volatile Memories: Challenges and Opportunities for Embedded System Architectures with Focus on  \nMachine Learning Applications  \nJörg Henkel 1 , Lokesh Siddhu 1 , Lars Bauer 1 , Jürgen Teich2 , Stefan Wildermann2 , Mehdi Tahoori 1 , Mahta Mayahinia 1 , Jeronimo Castrillon3 , AsifAli Khan3 , Hamid Farzaneh3 , João Paulo C. de Lima3 , Jian-Jia Chen4,5 , Christian Hakert4 , Kuan-Hsun Chen6 , Chia-Lin Yang7 , Hsiang-Yun Cheng8  \n1 Karlsruhe Institute of Technology (KIT), Germany 2 Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany  \n3 TU Dresden, Germany 4 TU Dortmund University, Germany 5 Lamarr Inst. for ML and AI, Germany  \n6 University of Twente, the Netherlands 7 National Taiwan University, Taiwan 8 Academia Sinica, Taiwan  \nABSTRACT  \nThis paper explores the challenges and opportunities of integrating non-volatile memories (NVMs) into embedded systems for machine learning. NVMs offer advantages such as increased memory density, lower power consumption, non-volatility, and compute-inmemory capabilities. The paper focuses on integrating NVMs into embedded systems, particularly in intermittent computing, where systems operate during periods of available energy. NVM technologies bring persistence closer to the CPU core, enabling efficient designs for energy-constrained scenarios. Next, computation in resistive NVMs is explored, highlighting its potential for accelerating machine learning algorithms. However, challenges related to reliability and device non-idealities need to be addressed. The paper also discusses memory-centric machine learning, leveraging NVMs to overcome the memory wall challenge. By optimizing memory layouts and utilizing probabilistic decision tree execution and neural network sparsity, NVM-based systems can improve cache behavior and reduce unnecessary computations. In conclusion, the paper emphasizes the need for further research and optimization for the widespread adoption of NVMs in embedded systems presenting relevant challenges, especially for machine learning applications.  \nKEYWORDS  \nNon Volatile Memories, Machine Learning, Compute In Memory, Design Space Exploration  \n1 INTRODUCTION  \nDue to advancements in manufacturing, the utilization of nonvolatile memories (NVMs) in embedded systems has gained traction [32, 56] . Many NVMs offer the advantage of storing multiple bits in a single memory cell (called a multi-level cell, MLC), increasing memory density. In addition, NVMs provide scalability, lower  \nPermission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission [and/or a fee. Request permissions from permissions@acm.org](and/or a fee. Request permissions from permissions@acm.org).  \nCASES ’23 Companion, September 17–22, 2023, Hamburg, Germany  \n© 2023 Copyright held by the owner/author(s) . Publication rights licensed to ACM. ACM ISBN 979-8-4007-0290-7/23/09. . . $15.00  \n[https://doi.org/10.1145/3607889.3609088](https://doi.org/10.1145/3607889.3609088)  \npower consumption, the ability to compute-in-memory (CIM), and non-volatility [40] . However, its adoption has been limited due to reduced write performance and endurance.  \nBoth academia and industry have explored many NVM technologies, such as Phase Change Memory (PCM), Spin-Transfer Torque RAM (STT-RAM), and Ferroelectric RAM (FRAM) . Furthermore, NVM technologies are gaining relevance across multiple application domains of embedded systems that are battery-p","cbCailS56SvmgjPU","https://ap.wps.com/l/cbCailS56SvmgjPU","pdf",1749986,1,10,"English","en",105,"# Abstract\n# Keywords\n# Introduction\n## Intermittent computing and energy unpredictability\n## NVM advantages for embedded systems\n## Computation in memory for accelerating ML","[{\"question\":\"What advantages do non-volatile memories provide for embedded machine learning systems?\",\"answer\":\"They increase memory density, reduce power consumption, retain data without power, and enable compute-in-memory capabilities that can accelerate machine learning workloads.\"},{\"question\":\"How does the paper relate NVMs to intermittent computing?\",\"answer\":\"It frames intermittent computing as energy-dependent operation with unpredictable power shortages, and argues that bringing persistence closer to the CPU core can reduce latency and energy overhead.\"},{\"question\":\"What challenges must be addressed for computation in resistive NVMs?\",\"answer\":\"The paper notes that reliability and device non-idealities are key obstacles that require further research and optimization.\"}]","Non-Volatile Memories - Challenges and Opportunities for Embedded System Architectures with Focus on Machine Learning Applications | PDF",1785736143,25,{"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},"non-volatile-memories-challenges-and-opportunities-for-embedded-system-architectures-with-focus-on-machine-learning-applications","",{"@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/technology/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/non-volatile-memories-challenges-and-opportunities-for-embedded-system-architectures-with-focus-on-machine-learning-applications/121538/",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},"What advantages do non-volatile memories provide for embedded machine learning systems?","Question",{"text":75,"@type":76},"They increase memory density, reduce power consumption, retain data without power, and enable compute-in-memory capabilities that can accelerate machine learning workloads.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the paper relate NVMs to intermittent computing?",{"text":80,"@type":76},"It frames intermittent computing as energy-dependent operation with unpredictable power shortages, and argues that bringing persistence closer to the CPU core can reduce latency and energy overhead.",{"name":82,"@type":73,"acceptedAnswer":83},"What challenges must be addressed for computation in resistive NVMs?",{"text":84,"@type":76},"The paper notes that reliability and device non-idealities are key obstacles that require further research and optimization.","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},[93,97,101,105,110,113,118,123,128,131,134],{"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":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":111,"slug":112},50,"technology",{"id":114,"doc_module":4,"doc_module_name":46,"category_name":115,"show_sort_weight":116,"slug":117},7,"Healthcare",40,"healthcare",{"id":119,"doc_module":4,"doc_module_name":46,"category_name":120,"show_sort_weight":121,"slug":122},8,"Research & Report",30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":21,"slug":133},"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]