[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122099-en":3,"doc-seo-122099-105":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},122099,8796095461610,"Oliver","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Optimizing Memristor-Based Synaptic Devices for Enhanced Energy Efficiency and Accuracy in Neuromorphic Machine Learning","The document addresses the energy and time inefficiency of the traditional Von Neumann computing architecture caused by frequent data transfers between external memory and the processor during deep learning and machine learning. It examines how memristive synaptic devices reduce transfers by integrating memory and processing, lowering energy consumption and operation latency. It further compares SGD and momentum-based variants and evaluates learning behavior, energy efficiency, and classification accuracy using TiO2-based nanoscale synaptic devices on MNIST and CIFAR datasets.","Received 26 September 2024, accepted 11 October 2024, date of publication 16 October 2024, date of current version 30 October 2024. Digital Object Identifier 10.1109/ACCESS.2024.3482110  \nOptimizing Memristor-Based Synaptic Devices for Enhanced Energy Efficiency and Accuracy in Neuromorphic Machine Learning  \nBAKI GÖKGÖZ1, TOLGA AYDıN2, AND FATIH GÜL3,(Member, IEEE)  \n1Department of Computer Technologies, Torul Vocational School, Gümüşhane University, 29100 Gümüşhane, Türkiye  \n2Faculty of Engineering, Computer Engineering Department, Atatürk University, 25030 Erzurum, Türkiye  \n3Faculty of Engineering and Architecture, Electrical and Electronics Engineering Department, Recep Tayyip Erdoğan University, 53100 Rize, Türkiye Corresponding author: Fatih Gül ([fatih.gul@erdogan.edu.tr](fatih.gul@erdogan.edu.tr))  \nThis work was supported by the Scientific Research Projects (BAP) Unit of Atatürk University, Erzurum, Türkiye, under Grant FDK-2022-9895 .  \nABSTRACT The traditional Von Neumann computing architecture, which necessitates data transfer between external memory and the processor, incurs significant energy and time costs when running deep learning (DL) and machine learning (ML) architectures. The primary issue with the energy and time efficiency of this architecture stems from the frequent and intensive data transfers between memory and the processor. Therefore, memristive synaptic devices are utilized to overcome this energy and time inefficiency while performing cognitive tasks. The fundamental working principle of memristive devices is to reduce the need for data transfer by combining memory and processing in the same location, thereby significantly decreasing both energy consumption and the time required for operations. However, to achieve the desired level of efficiency in terms of energy and time consumption from neuromorphic systems, the performance of these systems needs to be further improved with respect to accuracy and test error rates for classification applications. Achieving high accuracy performance in such deep learning or machine learning models necessitates optimization processes not only at the hardware level but also at the algorithmic level. In this context, this paper presents a comprehensive examination and comparison of the frequently used SGD and its momentum variants for deep learning and machine learning applications in memristor-based neuromorphic computing systems. The study thoroughly investigates the performance of critical metrics such as the learning properties, energy efficiency, and accuracy rates of the nano-scale titanium dioxide (TiO2) based synaptic device. The experimental results for the MNIST dataset showed AdaDelta 89.48%, AdaGrad 79.00%, Adam 79.13%, AdaMax 79.68%, Momentum 88.55%, Nadam 81.20%, RMSprop 84.91% and SGD 89.47%  \naccuracy. The experimental results for the CIFAR dataset showed AdaDelta 90.51%, AdaGrad 82.08%, Adam 83.10%, AdaMax 81.76%, Momentum 91.25%, Nadam 82.45%, RMSprop 88.11% and SGD 90.21% accuracy.  \nINDEX TERMS Deep learning, machine learning, memristors, neuromorphic computing, optimization algorithms, synapses.  \nI. INTRODUCTION  \nToday, machine learning and deep learning are rapidly becoming widespread and finding various application areas [1]. These methods, which began in the academic world,  \nThe associate editor coordinating the review of this manuscript and  \napproving it for publication was Berdakh Abibullaev  .  \nhave quickly grown to become significant in both academic and industrial applications. They are also indispensable components of data science [2] . Thanks to advancements in computer technologies, they have gained an important place in artificial intelligence fields [3] . However, the traditional Von-Neumann architecture falls short in methods inspired by biological neural networks [4] . This inadequacy is due  \nVOLUME 12, 2024  \n􀀊 2024 The Authors. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 Li","cbCaiqK3nz1W8M61","https://ap.wps.com/l/cbCaiqK3nz1W8M61","pdf",3306008,1,17,"English","en",105,"# Abstract\n# Introduction\n# Related Studies","[{\"question\":\"Why does the Von Neumann architecture create energy and time costs for deep learning and machine learning?\",\"answer\":\"It requires frequent, intensive data transfers between external memory and the processor, which makes energy use and runtime inefficient.\"},{\"question\":\"How do memristive synaptic devices improve energy and time efficiency in neuromorphic systems?\",\"answer\":\"They reduce data transfer by combining memory and processing at the same location, decreasing both energy consumption and operation time.\"},{\"question\":\"Which optimization approaches are compared, and what key results are reported?\",\"answer\":\"The study compares SGD and momentum variants. Experiments on MNIST and CIFAR report accuracy values across optimizers, with SGD achieving about 89.47% (MNIST) and 90.21% (CIFAR) in the provided results.\"}]","Optimizing Memristor-Based Synaptic Devices for Enhanced Energy Efficiency and Accuracy in Neuromorphic Machine Learning | PDF",1785808805,43,{"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},"optimizing-memristor-based-synaptic-devices-for-enhanced-energy-efficiency-and-accuracy-in-neuromorphic-machine-learning","",{"@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/optimizing-memristor-based-synaptic-devices-for-enhanced-energy-efficiency-and-accuracy-in-neuromorphic-machine-learning/122099/",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","2026-08-04",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 does the Von Neumann architecture create energy and time costs for deep learning and machine learning?","Question",{"text":75,"@type":76},"It requires frequent, intensive data transfers between external memory and the processor, which makes energy use and runtime inefficient.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How do memristive synaptic devices improve energy and time efficiency in neuromorphic systems?",{"text":80,"@type":76},"They reduce data transfer by combining memory and processing at the same location, decreasing both energy consumption and operation time.",{"name":82,"@type":73,"acceptedAnswer":83},"Which optimization approaches are compared, and what key results are reported?",{"text":84,"@type":76},"The study compares SGD and momentum variants. 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